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REVIEW 3 major objections 4 minor 203 references

Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data

T0 review · 3 major / 4 minor · reviewed 2026-08-02 · deepseek-v4-flash

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Pith's one-line read A two-stage framework that turns observational booking data into interpretable pricing rules raised airline upsell revenue per booking by 6.9% in a live pilot.

desk verdict Competent OR/ML deployment study with honest disclosure of prior work, but the 6.9% causal uplift is not as solid as the abstract suggests; the synthetic-control identification is the load-bearing weak point. read the letter →

arxiv 2607.14318 v1 pith:P2UMM67P submitted 2026-07-15 cs.LG

Youssef Drissi , Markus Ettl , Shivaram Subramanian , Wei Sun , Zack Xue This is my paper · ORCID
classification cs.LG
keywords prescriptiveanalyticscounterfactualestimationactiontreesmixed-integeroptimizationcolumngenerationairlineancillarypricingsyntheticcontrolinterpretablepolicies
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

COAT (Counterfactual Optimal Action Tree) claims that you can learn transparent, rule-based pricing policies from observational data by separating causal outcome estimation from a constrained tree-optimization step, and that such policies can be deployed in regulated real-world settings. The paper's evidence is a 17-week field pilot with a major airline in which the framework increased upsell revenue per booking by 6.9% relative to a synthetic control, with the airline projecting $50–150 million in additional annual premium seat revenue. The claim matters because it suggests that interpretability and operational constraints need not come at the cost of performance — that optimization can serve as the decision layer that converts predictive AI into auditable, deployable actions.

What carries the argument

The central object is the 'action tree' built from a policy graph in which each root-to-leaf path is a complete decision rule (a conjunction of feature-value tests plus an action). The framework decouples estimation — a doubly robust counterfactual model over rich covariates — from optimization, which selects a small non-overlapping set of paths maximizing counterfactual reward. Column generation drives scalability: the restricted master problem handles rule selection and inter-rule constraints, while the pricing subproblem (a shortest-path problem) generates new candidate rules subject to intra-rule constraints like length limits and forbidden feature combinations. Evaluation relies on synt

What would settle it

A randomized A/B test on a subset of markets (or a placebo analysis applying the same synthetic-control procedure to a randomly chosen pseudo-treatment date) that fails to reproduce a revenue-per-booking uplift around 6.9% would falsify the central claim. Equivalently, if control-market weights estimated from a much longer pre-period produce a near-zero post-pilot gap, the reported effect is an artifact of the short pre-window.

Watch

Extended reading notes

Core claim

The central discovery is that a policy expressed as a small set of multiway decision rules — each a root-to-leaf path assigning a price markup — can be optimized directly over counterfactual purchase probabilities estimated with a doubly robust model, and that the resulting tree performs well in the field. Using a path-based mixed-integer formulation solved by column generation, the method selects non-overlapping rules that maximize expected revenue subject to business constraints (capacity, monotonicity, guardrails on price changes). In the airline pilot, the selected trees raised prices for price-insensitive segments, lowered them for price-sensitive segments, and produced a 6.9% uplift in

Load-bearing premise

The synthetic control counterfactual is valid: absent the intervention, the 12 test markets would have tracked their weighted combination of 30 control markets, with weights estimated from only nine pre-pilot weeks and no guarantee against time-varying confounders.

Editorial extensions

If this is right

  • Decoupling estimation from optimization lets the policy tree use only a small, deployment-ready feature set while the estimator exploits rich data, making real-time execution feasible.
  • The path-based representation makes it natural to enforce inter-rule and intra-rule constraints — capacity, fairness, monotonicity, and stability guardrails — that are cumbersome in arc-based tree formulations.
  • The live pilot results imply that interpretable, constrained policies can match or beat manual pricing rules in a regulated industry, supporting the case for prescriptive AI as a decision layer.
  • The framework is not limited to pricing: any discrete-action decision problem with counterfactual outcomes and auditability requirements fits the same estimation-plus-optimization template.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The 6.9% uplift rests on a synthetic control built from just nine pre-pilot weeks; if unobserved shocks drove the post-pilot gap, the causal effect would be overstated, and a randomized rollout on a few markets would be the cleanest check.
  • The guardrail constraints that bound price increases and decreases likely cap the achievable revenue lift; relaxing them could improve performance but would risk the very fairness and acceptance properties that made deployment possible.
  • The paper's pragmatic treatment of prices with zero historical support (outcome-only extrapolation) is a stopgap; a principled exploration or bounded-bias estimator would strengthen the causal claims in the expanding-price-grid setting.
  • Similar decoupled frameworks could be tested in other regulated domains — insurance, credit, telecom — where rule-based transparency is a legal or operational requirement.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper introduces COAT, a two-stage framework for learning interpretable prescriptive policies from observational data. The first stage estimates counterfactual outcomes (using a doubly robust estimator, then a calibrated outcome model in deployment); the second stage formulates policy learning as a path-based mixed-integer program solved by column generation, with support for inter-rule and intra-rule operational constraints. Theoretical results characterize the path space, exact state aggregation, and the effect of depth/categorical splits. The central empirical claim is a 17-week live airline pilot in which COAT pricing policies produced a 6.9% uplift in upsell revenue per booking relative to a synthetic control, with a placebo permutation p-value of 0.032 and an airline projection of $50–$150 million in incremental annual revenue. The paper frames the pilot as evidence that the decoupled estimation-plus-optimization design yields deployable, interpretable policies that improve realized revenue in a regulated setting.

Significance. If the causal claim holds, the paper is significant for prescriptive analytics: it combines causal estimation, large-scale optimization, and interpretable policy representation in a genuine field deployment, with explicit operational constraints and a synthetic-control evaluation. The strengths of the manuscript include a clear decoupled architecture, a nontrivial column-generation formulation with proofs of exactness and aggregation, detailed treatment of guardrail constraints, and an unusually candid electronic companion that acknowledges positivity violations and the resolution floor of the placebo test. The theoretical and algorithmic contributions are modest but serviceable; the main value is the field evidence. However, the headline revenue uplift rests on a synthetic control estimated from nine pre-pilot weeks with thirty donor weights, non-random market selection, and an outcome-based pilot extension. These features make the central empirical claim fragile as it stands, even though the concerns may be addressable with additional sensitivity analyses.

major comments (3)
  1. [EC.5.1, Eq. (EC.10); Section 6.6.3] The SCM weights are estimated from T0=9 pre-pilot weeks for J=30 donor markets. The least-squares problem (EC.10) is underdetermined (J > T0), so many weight vectors fit the pre-period almost perfectly. A good pre-intervention fit is therefore not evidence that the synthetic control tracks the treated market's counterfactual trajectory. The paper reports non-uniform weight distributions, but no regularization, penalty, cross-validation, or stability analysis for the weights. This is load-bearing because the 6.9% uplift is defined as the post-period gap relative to this synthetic control. I would ask for a sensitivity analysis: e.g., restrict donors to a small matched subset, add L1/L2 penalties on weights, report leave-one-market-out intervals, and show that the estimated effect is robust across these choices.
  2. [EC.5.2; Section 6.7.1] The placebo p-value is 0.032, which is exactly 1/31—the smallest value attainable with 31 units. Moreover, the treated RMSPE ratio (11.0) is only slightly above the largest placebo (10.4), with the third placebo much lower (3.2). A small perturbation in the data or donor pool would move the p-value to 0.065. Thus the reported rejection of the null is at the resolution floor and is not as strong as the text implies. The manuscript should state this limitation explicitly, report the full distribution of placebo ratios, and provide a confidence interval or other uncertainty measure for the 6.9% average effect rather than only a permutation p-value.
  3. [Section 6.6.2 and Section 6.7] The pilot was initially planned for 12 weeks and extended because of strong early performance; the paper then reports the full Weeks 10–26 average, including the extension period. The post-intervention window is therefore selected on early outcomes, which can bias the reported average treatment effect upward even under a valid synthetic control. In addition, the 12 test markets were selected non-randomly across route lengths and demand characteristics. The paper acknowledges vulnerability to time-varying confounders, but the evaluation should include pre-specified or sensitivity analyses: e.g., effect estimates at Week 15 and Week 21 (before extension decisions), a comparison of pre-specified vs. extended windows, and a discussion of how non-random market selection could affect the SCM estimate. Without these, the headline 6.9% is not established as a causal effect.
minor comments (4)
  1. [Section 6.4 / EC.3] The paper acknowledges that newly introduced prices have zero historical support, so the positivity assumption fails for exactly the actions the policy may select. The outcome-only strategy is a reasonable practical choice, but the text should more explicitly state that the counterfactual inputs to the optimization have no consistency guarantee in those regions; the guardrail constraints mitigate but do not eliminate this risk. This is partly acknowledged, so I list it as a clarity issue.
  2. [Section 6.4] The elasticity calibration step relies on analyst-provided elasticity ranges and per-market price grids. These are free parameters that affect the counterfactual predictions and hence the policy. The paper should provide a sensitivity analysis or at least a clear statement of how these choices affect the downstream revenue estimate.
  3. [Section 5.4, Proposition 5] The lower bound in Proposition 5 assumes a policy that requires distinguishing all η^k profiles. For a pricing application with a finite action set, this may be stronger than necessary; the statement is correct as a representation-theoretic bound, but the text should clarify that the bound is worst-case over policies, not typical for the application.
  4. [Throughout] There are a number of typographical and formatting issues (e.g., 'T ree', 'F ramework', 'na ¨ ïve'). These do not affect substance but should be cleaned up before publication.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the headline uplift is an externally evaluated field outcome, not a refit of its inputs.

full rationale

The paper's chain is: (i) estimate counterfactual purchase probabilities with a DR/outcome-only model; (ii) optimize a constrained action tree over those estimates; (iii) deploy in test markets; (iv) measure realized upsell revenue per booking against a synthetic control constructed from 30 control markets using a 9-week pre-period. No step is equivalent to its input by construction. The 6.9% figure is a post-hoc difference between realized test-market revenue and the SCM counterfactual, not a quantity fitted from the counterfactual model or from the SCM weights. The SCM pre-period fit is a property of the least-squares weights, but the post-period gap uses new data and is not forced. The paper's own EC.3 flags that unseen-price counterfactuals rely on outcome-only extrapolation and that positivity fails; this is a stated identification limitation, not a hidden circularity. The citation to Anonymous 2022 for the path-based formulation is a self-citation, but it is not load-bearing for the headline: the optimization is also supported by in-paper Propositions 1-5 and Theorem 2, and the field pilot provides external evidence. Placebo p=0.032 is a resolution floor, and the extension after good performance is a selection concern; both affect robustness, not circularity.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The framework is modular and uses standard causal-inference and optimization machinery. The central empirical claim rests on several tuning choices (price grids, elasticity calibration, guardrail bounds, tree-size limits) and on assumptions that the paper itself acknowledges are violated for unseen prices. The theoretical results are mostly counting and LP-size arguments rather than new optimization theory.

free parameters (6)
  • Per-market price grids = 10-25 discrete price points; coarse $5 increments on long-haul
    Markup grids defined from historical price distributions and business input, not optimized end-to-end (Section 6.4).
  • Elasticity calibration bounds = Analyst-provided elasticity ranges, not specified numerically
    Price-response curves adjusted to satisfy economically plausible elasticity bounds and anchored at factual observations (Section 6.4).
  • Maximum tree size l = 50 rules per market
    User-defined upper bound imposed by the airline after experimentation (Section 6.5).
  • Conversion-rate tolerance band = Not specified
    Surrogate capacity constraints keep predicted upgrade conversion within a band around historical levels (Section 6.5).
  • PDR/PIR guardrail bounds = Not specified
    Bounds on the share of price increases/decreases relative to historical pricing; parameters are not reported (Eq. 14).
  • Slack penalty coefficients c_i = Sufficiently large; exact magnitude not reported
    Penalty coefficients are used to force exact coverage in the MIP but their values are not specified (Section 4.2).
assumptions (6)
  • domain assumption Unconfoundedness, positivity, and consistency
    Standard causal identification assumptions stated in Section 3.1. Positivity is later acknowledged to fail for newly introduced prices, forcing outcome-only extrapolation (EC.3).
  • domain assumption Outcome-model extrapolation to unseen prices is meaningful
    When a price has zero historical support, the paper relies on an outcome-only model; no consistency guarantee is claimed (EC.3).
  • domain assumption Synthetic control validity
    The SCM counterfactual requires that pre-treatment fit carries over post-treatment; only 9 pre-period weeks are used and markets were selected non-randomly (EC.5.1).
  • standard math Selected paths can always be assembled into a valid multiway tree
    Theorem 1 proof (EC.4.1) relies on the fixed feature order in the policy graph and the exact-coverage constraint to construct a trie.
  • standard math Column generation with shortest-path pricing finds improving columns
    Standard LP duality/reduced-cost machinery; the pricing subproblem is assumed tractable and solvable as a shortest-path problem.
  • domain assumption Fixed feature order in the policy graph does not materially restrict the optimal policy
    The path-based representation imposes a canonical feature ordering; this changes the policy class compared to unrestricted binary trees and is not argued to be without loss.

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Cite this review

Pith. "Pith review of Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data." pith.science (2026). https://pith.science/paper/P2UMM67P

@misc{pith2026260714318,
  author       = {Pith},
  title        = {Pith review of: Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P2UMM67P}},
  note         = {Machine review of arXiv:2607.14318}
}
abstract

We introduce COAT (Counterfactual Optimal Action Tree), a framework for learning interpretable prescriptive policies from observational data. COAT combines counterfactual outcome estimation with large-scale mixed-integer optimization, using column generation to translate causal predictions into feasible, transparent decisions under business and regulatory constraints. We apply COAT to airline ancillary pricing, a setting characterized by complex business rules and limited experimental flexibility. In a 17-week field pilot with a major global airline, COAT increased upsell revenue per booking by 6.9%, with the airline projecting \$50-\$150 million in incremental annual premium seat revenue across eligible domestic markets. The success of the pilot led to scaled adoption and informed broader AI-driven decision initiatives within the organization.

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Reference graph

Works this paper leans on

203 extracted references · 11 linked inside Pith

  1. [1]

    Proceedings of the sixteenth annual ACM symposium on Theory of computing , pages=

    A new polynomial-time algorithm for linear programming , author=. Proceedings of the sixteenth annual ACM symposium on Theory of computing , pages=

  2. [2]

    International Conference on Artificial Intelligence and Statistics , pages=

    Manifold-aligned counterfactual explanations for neural networks , author=. International Conference on Artificial Intelligence and Statistics , pages=

  3. [3]

    Available at SSRN 3355211 , year=

    Doubly adaptive cascading bandits with user abandonment , author=. Available at SSRN 3355211 , year=

  4. [4]

    2024 , note =

    Watson Studio AutoAI , howpublished =. 2024 , note =

    2024

  5. [5]

    Chen, Tianqi and Guestrin, Carlos , booktitle=

  6. [6]

    International Conference on Learning Representations , year =

    POETREE: Interpretable Policy Learning with Adaptive Decision Trees , author=. International Conference on Learning Representations , year =

  7. [7]

    Operations Research Perspectives , volume=

    Prescriptive price optimization using optimal regression trees , author=. Operations Research Perspectives , volume=. 2023 , publisher=

    2023

  8. [8]

    USSR Computational Mathematics and Mathematical Physics , volume=

    Polynomial algorithms in linear programming , author=. USSR Computational Mathematics and Mathematical Physics , volume=. 1980 , publisher=

    1980

Show all 203 references
  1. [9]

    Proceedings of the AAAI Conference on Artificial Intelligence , year =

    Anonymous , title =. Proceedings of the AAAI Conference on Artificial Intelligence , year =

  2. [10]

    Journal of the American Statistical Association , volume=

    Estimation and inference of heterogeneous treatment effects using random forests , author=. Journal of the American Statistical Association , volume=. 2018 , publisher=

    2018

  3. [11]

    2022 , publisher=

    Revenue management in the lodging industry: origins to the last frontier , author=. 2022 , publisher=

    2022

  4. [12]

    2025 , url =

    How Delta is using. 2025 , url =

    2025

  5. [13]

    2025 , month = aug, day =

    Delta Air assures. 2025 , month = aug, day =

    2025

  6. [14]

    2025 , url =

    Delta responds to misinformation around. 2025 , url =

    2025

  7. [15]

    Journal of global optimization , volume=

    Optimal decision trees for categorical data via integer programming , author=. Journal of global optimization , volume=. 2021 , publisher=

    2021

  8. [16]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Learning optimal classification trees using a binary linear program formulation , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  9. [17]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Constrained Prescriptive Trees via Column Generation , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  10. [18]

    Airline Ancillary Revenue Reaches Record \ 117.9 Billion Worldwide for 2023

    IdeaWorks. Airline Ancillary Revenue Reaches Record \ 117.9 Billion Worldwide for 2023

    2023

  11. [19]

    DATA APPLICATION CATEGORY 25 – FARE BY RULE

    ATPCO. DATA APPLICATION CATEGORY 25 – FARE BY RULE

  12. [20]

    ATPCO & Fare Filing Assistance

    TravelPort. ATPCO & Fare Filing Assistance

  13. [21]

    2025 , month = jul, day =

    Delta moves toward eliminating set prices in favor of. 2025 , month = jul, day =

    2025

  14. [22]

    Annual Review of Statistics and Its Application , volume=

    Interpretable machine learning for discovery: Statistical challenges and opportunities , author=. Annual Review of Statistics and Its Application , volume=. 2023 , publisher=

    2023

  15. [23]

    Statistic Surveys , volume=

    Interpretable machine learning: Fundamental principles and 10 grand challenges , author=. Statistic Surveys , volume=. 2022 , publisher=

    2022

  16. [24]

    Communications of the ACM , volume=

    Techniques for interpretable machine learning , author=. Communications of the ACM , volume=. 2019 , publisher=

    2019

  17. [25]

    Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining , pages=

    Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission , author=. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining , pages=

  18. [26]

    Explainable Artificial Intelligence (

    Arrieta, Alejandro Barredo and D. Explainable Artificial Intelligence (. Information fusion , volume=. 2020 , publisher=

    2020

  19. [27]

    Nature machine intelligence , volume=

    Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , author=. Nature machine intelligence , volume=. 2019 , publisher=

    2019

  20. [28]

    arXiv preprint arXiv:1912.02572 , year=

    Dynamic pricing on e-commerce platform with deep reinforcement learning: A field experiment , author=. arXiv preprint arXiv:1912.02572 , year=

    1912 arXiv

  21. [29]

    Business Economics , volume=

    Science of price experimentation at Amazon , author=. Business Economics , volume=. 2023 , publisher=

    2023

  22. [30]

    European Journal of Operational Research , volume=

    High price or low price? An experimental study on a markdown pricing policy , author=. European Journal of Operational Research , volume=. 2020 , publisher=

    2020

  23. [31]

    econometrica , volume=

    Large sample properties of matching estimators for average treatment effects , author=. econometrica , volume=. 2006 , publisher=

    2006

  24. [32]

    Biometrika , volume=

    Quasi-oracle estimation of heterogeneous treatment effects , author=. Biometrika , volume=. 2021 , publisher=

    2021

  25. [33]

    Journal of the American statistical Association , volume=

    Model-based direct adjustment , author=. Journal of the American statistical Association , volume=. 1987 , publisher=

    1987

  26. [34]

    Statistical science: a review journal of the Institute of Mathematical Statistics , volume=

    Matching methods for causal inference: A review and a look forward , author=. Statistical science: a review journal of the Institute of Mathematical Statistics , volume=

  27. [35]

    Econometrica , volume=

    Efficient estimation of average treatment effects using the estimated propensity score , author=. Econometrica , volume=. 2003 , publisher=

    2003

  28. [36]

    Operations research , volume=

    Clearance pricing optimization for a fast-fashion retailer , author=. Operations research , volume=. 2012 , publisher=

    2012

  29. [37]

    Information Systems Research , volume=

    Socialize more, pay less: Randomized field experiments on social pricing , author=. Information Systems Research , volume=. 2022 , publisher=

    2022

  30. [38]

    Advances in neural information processing systems , volume=

    Examples are not enough, learn to criticize! criticism for interpretability , author=. Advances in neural information processing systems , volume=

  31. [39]

    Advances in neural information processing systems , volume=

    Towards robust interpretability with self-explaining neural networks , author=. Advances in neural information processing systems , volume=

  32. [40]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  33. [41]

    Journal of Theoretical and Applied Electronic Commerce Research , volume=

    The Impact of AI-Personalized Recommendations on Clicking Intentions: Evidence from Chinese E-Commerce , author=. Journal of Theoretical and Applied Electronic Commerce Research , volume=. 2025 , publisher=

    2025

  34. [42]

    Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=

    Promotheus: An end-to-end machine learning framework for optimizing markdown in online fashion e-commerce , author=. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=

  35. [43]

    Production and Operations Management , volume=

    Dynamic pricing and information disclosure for fresh produce: An artificial intelligence approach , author=. Production and Operations Management , volume=. 2022 , publisher=

    2022

  36. [44]

    Financial risk management and explainable, trustworthy, responsible

    Fritz-Morgenthal, Sebastian and Hein, Bernhard and Papenbrock, Jochen , journal=. Financial risk management and explainable, trustworthy, responsible. 2022 , publisher=

    2022

  37. [45]

    Future Business Journal , volume=

    Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN) , author=. Future Business Journal , volume=. 2025 , publisher=

    2025

  38. [46]

    Journal of Open Innovation: Technology, Market, and Complexity , volume=

    Enhancing supply chain management with deep learning and machine learning techniques: A review , author=. Journal of Open Innovation: Technology, Market, and Complexity , volume=. 2024 , publisher=

    2024

  39. [47]

    Human resource management review , volume=

    An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes , author=. Human resource management review , volume=. 2023 , publisher=

    2023

  40. [48]

    PRICING DATA: Powering modern airline retailing with data

    ATPCO. PRICING DATA: Powering modern airline retailing with data

  41. [49]

    2006 , publisher=

    The theory and practice of revenue management , author=. 2006 , publisher=

    2006

  42. [50]

    2016 , publisher=

    Straight and level: Practical airline economics , author=. 2016 , publisher=

    2016

  43. [51]

    2021 , publisher=

    Pricing and revenue optimization , author=. 2021 , publisher=

    2021

  44. [52]

    Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , pages=

    Ranking distillation: Learning compact ranking models with high performance for recommender system , author=. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , pages=

  45. [53]

    International Conference on Machine Learning , pages=

    Learning theory and algorithms for revenue optimization in second price auctions with reserve , author=. International Conference on Machine Learning , pages=

  46. [55]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Unifying heterogeneous classifiers with distillation , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  47. [56]

    2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) , pages=

    Interpreting social media-based substance use prediction models with knowledge distillation , author=. 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) , pages=. 2018 , organization=

    2018

  48. [57]

    Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , pages=

    A general knowledge distillation framework for counterfactual recommendation via uniform data , author=. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , pages=

  49. [58]

    Neurocomputing , volume=

    A novel enhanced collaborative autoencoder with knowledge distillation for top-N recommender systems , author=. Neurocomputing , volume=. 2019 , publisher=

    2019

  50. [59]

    International conference on machine learning , pages=

    Learning representations for counterfactual inference , author=. International conference on machine learning , pages=

  51. [60]

    arXiv preprint arXiv:1512.03542 , year=

    Distilling knowledge from deep networks with applications to healthcare domain , author=. arXiv preprint arXiv:1512.03542 , year=

    arXiv

  52. [61]

    Artificial intelligence , volume=

    Explanation in artificial intelligence: Insights from the social sciences , author=. Artificial intelligence , volume=. 2019 , publisher=

    2019

  53. [62]

    arXiv preprint arXiv:1711.09784 , year=

    Distilling a neural network into a soft decision tree , author=. arXiv preprint arXiv:1711.09784 , year=

    arXiv

  54. [63]

    Advances in neural information processing systems , pages=

    A unified approach to interpreting model predictions , author=. Advances in neural information processing systems , pages=

  55. [64]

    arXiv preprint arXiv:1702.08608 , year=

    Towards a rigorous science of interpretable machine learning , author=. arXiv preprint arXiv:1702.08608 , year=

    arXiv

  56. [65]

    Applied Bayesian modeling and causal inference from incomplete-data perspectives , volume=

    The propensity score with continuous treatments , author=. Applied Bayesian modeling and causal inference from incomplete-data perspectives , volume=

  57. [66]

    Information processing letters , volume=

    Constructing optimal binary decision trees is NP-complete , author=. Information processing letters , volume=

  58. [67]

    2013 , publisher=

    A probabilistic theory of pattern recognition , author=. 2013 , publisher=

    2013

  59. [68]

    2017 , publisher=

    Classification and regression trees , author=. 2017 , publisher=

    2017

  60. [69]

    1984 , publisher=

    Classification and regression trees , author=. 1984 , publisher=

    1984

  61. [70]

    Advances in neural information processing systems , pages=

    Lightgbm: A highly efficient gradient boosting decision tree , author=. Advances in neural information processing systems , pages=

  62. [71]

    Proceedings of the 28th International Conference on International Conference on Machine Learning , pages=

    Doubly robust policy evaluation and learning , author=. Proceedings of the 28th International Conference on International Conference on Machine Learning , pages=

  63. [72]

    Proceedings of the 34th International Conference on Machine Learning-Volume 70 , pages=

    Recursive partitioning for personalization using observational data , author=. Proceedings of the 34th International Conference on Machine Learning-Volume 70 , pages=

  64. [73]

    American Economic Review , volume=

    Synthetic difference-in-differences , author=. American Economic Review , volume=. 2021 , publisher=

    2021

  65. [74]

    Proceedings of the National Academy of Sciences , volume=

    Recursive partitioning for heterogeneous causal effects , author=. Proceedings of the National Academy of Sciences , volume=. 2016 , publisher=

    2016

  66. [75]

    Machine learning , volume=

    Induction of decision trees , author=. Machine learning , volume=. 1986 , publisher=

    1986

  67. [76]

    Column generation , pages=

    A primer in column generation , author=. Column generation , pages=. 2005 , publisher=

    2005

  68. [77]

    SIAM Journal on computing , volume=

    Finding the k shortest paths , author=. SIAM Journal on computing , volume=. 1998 , publisher=

    1998

  69. [78]

    Journal of the Operational Research Society , volume=

    Finding the K least cost paths in an acyclic activity network , author=. Journal of the Operational Research Society , volume=. 1980 , publisher=

    1980

  70. [79]

    A Scalable

    Zhu, Haoran and Murali, Pavankumar and Phan, Dzung and Nguyen, Lam and Kalagnanam, Jayant , booktitle =. A Scalable

  71. [80]

    Proceedings of the national academy of sciences , volume=

    Metalearners for estimating heterogeneous treatment effects using machine learning , author=. Proceedings of the national academy of sciences , volume=. 2019 , publisher=

    2019

  72. [81]

    International Conference on Machine Learning , pages=

    Estimating individual treatment effect: generalization bounds and algorithms , author=. International Conference on Machine Learning , pages=

  73. [82]

    Advances in Neural Information Processing Systems , volume=

    Exploring large feature spaces with hierarchical multiple kernel learning , author=. Advances in Neural Information Processing Systems , volume=

  74. [83]

    ICML , year=

    Efficient rule ensemble learning using hierarchical kernels , author=. ICML , year=

  75. [84]

    International Cross-Domain Conference for Machine Learning and Knowledge Extraction , pages=

    Explainable reinforcement learning: A survey , author=. International Cross-Domain Conference for Machine Learning and Knowledge Extraction , pages=. 2020 , organization=

    2020

  76. [85]

    Joint European Conference on Machine Learning and Knowledge Discovery in Databases , pages=

    Toward interpretable deep reinforcement learning with linear model u-trees , author=. Joint European Conference on Machine Learning and Knowledge Discovery in Databases , pages=. 2018 , organization=

    2018

  77. [86]

    Management Science , volume=

    A suggested computation for maximal multi-commodity network flows , author=. Management Science , volume=. 1958 , publisher=

    1958

  78. [87]

    Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages =

    Ye, Peng and Qian, Julian and Chen, Jieying and Wu, Chen-hung and Zhou, Yitong and De Mars, Spencer and Yang, Frank and Zhang, Li , title =. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages =

  79. [88]

    arXiv preprint arXiv:1503.02531 , year=

    Distilling the knowledge in a neural network , author=. arXiv preprint arXiv:1503.02531 , year=

    arXiv

  80. [89]

    The Annals of Applied Statistics , volume=

    Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model , author=. The Annals of Applied Statistics , volume=. 2015 , publisher=

    2015

  81. [90]

    Machine Learning , volume=

    Supersparse linear integer models for optimized medical scoring systems , author=. Machine Learning , volume=. 2016 , publisher=

    2016

  82. [91]

    Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , pages=

    Interpretable decision sets: A joint framework for description and prediction , author=. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , pages=

  83. [92]

    arXiv preprint arXiv:1907.03419 , year=

    The Price of Interpretability , author=. arXiv preprint arXiv:1907.03419 , year=

    1907 arXiv

  84. [93]

    Advances in neural information processing systems , pages=

    Extracting tree-structured representations of trained networks , author=. Advances in neural information processing systems , pages=

  85. [94]

    Why should i trust you?

    " Why should i trust you?" Explaining the predictions of any classifier , author=. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , pages=

  86. [95]

    Thirty-Second AAAI Conference on Artificial Intelligence , year=

    Anchors: High-precision model-agnostic explanations , author=. Thirty-Second AAAI Conference on Artificial Intelligence , year=

  87. [96]

    Journal of Machine Learning Research , volume=

    How to explain individual classification decisions , author=. Journal of Machine Learning Research , volume=

  88. [97]

    AAAI Spring Syposium on Knowledge Representation and Reasoning (KRR): Integrating Symbolic and Neural Approaches , volume=

    Towards extracting faithful and descriptive representations of latent variable models , author=. AAAI Spring Syposium on Knowledge Representation and Reasoning (KRR): Integrating Symbolic and Neural Approaches , volume=

  89. [98]

    The Annals of Applied Statistics , volume=

    Predictive learning via rule ensembles , author=. The Annals of Applied Statistics , volume=. 2008 , publisher=

    2008

  90. [99]

    Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , pages=

    Model compression , author=. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , pages=

  91. [100]

    Manufacturing & Service Operations Management , volume=

    Analytics for an online retailer: Demand forecasting and price optimization , author=. Manufacturing & Service Operations Management , volume=. 2016 , publisher=

    2016

  92. [101]

    Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , pages=

    Customized regression model for airbnb dynamic pricing , author=. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , pages=

  93. [102]

    Available at SSRN 2972985 , year=

    Personalized dynamic pricing with machine learning: High dimensional features and heterogeneous elasticity , author=. Available at SSRN 2972985 , year=

  94. [103]

    Available at SSRN 2579462 , year=

    A statistical learning approach to personalization in revenue management , author=. Available at SSRN 2579462 , year=

  95. [104]

    Available at SSRN 3242529 , year=

    Detecting customer trends for optimal promotion targeting , author=. Available at SSRN 3242529 , year=

  96. [105]

    arXiv preprint arXiv:1609.07574 , year=

    Dynamic pricing in high-dimensions , author=. arXiv preprint arXiv:1609.07574 , year=

    arXiv

  97. [106]

    Available at SSRN 3127719 , year=

    The value of personalized pricing , author=. Available at SSRN 3127719 , year=

  98. [107]

    International Conference on Machine Learning , pages=

    Decision trees for decision-making under the predict-then-optimize framework , author=. International Conference on Machine Learning , pages=

  99. [108]

    Predict, then Optimize

    Smart "Predict, then Optimize" , author=. 2017 , eprint=

    2017

  100. [109]

    Advances in Neural Information Processing Systems , pages=

    Optimization over continuous and multi-dimensional decisions with observational data , author=. Advances in Neural Information Processing Systems , pages=

  101. [110]

    arXiv preprint arXiv:1705.10883 , year=

    Optimization of tree ensembles , author=. arXiv preprint arXiv:1705.10883 , year=

    arXiv

  102. [111]

    Available at SSRN 2986630 , year=

    Optimizing objective functions determined from random forests , author=. Available at SSRN 2986630 , year=

  103. [112]

    Operations Research , volume=

    Strong optimal classification trees , author=. Operations Research , volume=

  104. [113]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Learning optimal and fair decision trees for non-discriminative decision-making , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  105. [114]

    Proceedings of the AAAI Conference on Artificial Intelligence , year=

    Constrained Prescriptive Trees via Column Generation , author=. Proceedings of the AAAI Conference on Artificial Intelligence , year=

  106. [115]

    arXiv preprint arXiv:2108.13628 , year=

    Learning optimal prescriptive trees from observational data , author=. arXiv preprint arXiv:2108.13628 , year=

    arXiv

  107. [116]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Dynamic learning of sequential choice bandit problem under marketing fatigue , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  108. [117]

    Tsiourvas, Asterios and Sun, Wei and Perakis, Georgia , booktitle =. Causal

  109. [118]

    Learning prescriptive

    Sun, Wei and Tsiourvas, Asterios , booktitle=. Learning prescriptive

  110. [119]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Scalable optimal multiway-split decision trees with constraints , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  111. [120]

    Machine Learning , volume=

    Optimal policy trees , author=. Machine Learning , volume=. 2022 , publisher=

    2022

  112. [121]

    Data Mining and Knowledge Discovery , volume=

    Building decision trees with constraints , author=. Data Mining and Knowledge Discovery , volume=. 2003 , publisher=

    2003

  113. [122]

    Operations Research , volume=

    Offline multi-action policy learning: Generalization and optimization , author=. Operations Research , volume=. 2023 , publisher=

    2023

  114. [123]

    ACM computing surveys (CSUR) , volume=

    A survey of methods for explaining black box models , author=. ACM computing surveys (CSUR) , volume=. 2018 , publisher=

    2018

  115. [124]

    Electronics , volume=

    Machine learning interpretability: A survey on methods and metrics , author=. Electronics , volume=. 2019 , publisher=

    2019

  116. [125]

    International Journal of Information Management , volume=

    Prescriptive analytics: Literature review and research challenges , author=. International Journal of Information Management , volume=. 2020 , publisher=

    2020

  117. [126]

    Machine Learning Proceedings 1995 , pages=

    Efficient algorithms for finding multi-way splits for decision trees , author=. Machine Learning Proceedings 1995 , pages=. 1995 , publisher=

    1995

  118. [127]

    International Conference on Machine Learning , pages=

    Model Distillation for Revenue Optimization: Interpretable Personalized Pricing , author=. International Conference on Machine Learning , pages=

  119. [128]

    Journal of applied statistics , volume=

    A method of choosing multiway partitions for classification and decision trees , author=. Journal of applied statistics , volume=. 1991 , publisher=

    1991

  120. [129]

    Operations Research , volume=

    The big data newsvendor: Practical insights from machine learning , author=. Operations Research , volume=. 2019 , publisher=

    2019

  121. [130]

    Machine Learning , volume=

    Optimal classification trees , author=. Machine Learning , volume=. 2017 , publisher=

    2017

  122. [131]

    arXiv preprint arXiv:1605.02347 , year=

    The power and limits of predictive approaches to observational-data-driven optimization , author=. arXiv preprint arXiv:1605.02347 , year=

    arXiv

  123. [132]

    Management Science , volume=

    From predictive to prescriptive analytics , author=. Management Science , volume=. 2020 , publisher=

    2020

  124. [133]

    INFORMS Journal on Optimization , volume=

    Optimal prescriptive trees , author=. INFORMS Journal on Optimization , volume=. 2019 , publisher=

    2019

  125. [134]

    Mathematical Programming , pages=

    Strong mixed-integer programming formulations for trained neural networks , author=. Mathematical Programming , pages=. 2020 , publisher=

    2020

  126. [135]

    2018 , publisher=

    Interpretable optimal stopping , author=. 2018 , publisher=

    2018

  127. [136]

    arXiv preprint arXiv:1705.08504 , pages=

    Interpreting predictive models for human-in-the-loop analytics , author=. arXiv preprint arXiv:1705.08504 , pages=

    arXiv

  128. [137]

    Available at SSRN 3069690 , year=

    Mining optimal policies: A pattern recognition approach to model analysis , author=. Available at SSRN 3069690 , year=

  129. [138]

    Management Science , year=

    Mostly exploration-free algorithms for contextual bandits , author=. Management Science , year=

  130. [139]

    Manufacturing & Service Operations Management , volume=

    Data analytics in operations management: A review , author=. Manufacturing & Service Operations Management , volume=. 2020 , publisher=

    2020

  131. [140]

    Machine learning , volume=

    Learning decision lists , author=. Machine learning , volume=. 1987 , publisher=

    1987

  132. [141]

    , author=

    Toward Attribute Efficient Learning of Decision Lists and Parities. , author=. Journal of Machine Learning Research , volume=

  133. [142]

    Interpretable Machine Learning , author=

  134. [143]

    Synthetic control methods for comparative case studies: Estimating the effect of

    Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens , journal=. Synthetic control methods for comparative case studies: Estimating the effect of. 2010 , publisher=

    2010

  135. [144]

    Journal of Economic perspectives , volume=

    The state of applied econometrics: Causality and policy evaluation , author=. Journal of Economic perspectives , volume=. 2017 , publisher=

    2017

  136. [145]

    American economic review , volume=

    The economic costs of conflict: A case study of the Basque Country , author=. American economic review , volume=. 2003 , publisher=

    2003

  137. [146]

    Handbook on economics and information systems , volume=

    Dynamic pricing in the airline industry , author=. Handbook on economics and information systems , volume=. 2006 , publisher=

    2006

  138. [147]

    Proceedings of the 25th ACM SIGKDD International Conference on knowledge discovery & data mining , pages=

    Dynamic pricing for airline ancillaries with customer context , author=. Proceedings of the 25th ACM SIGKDD International Conference on knowledge discovery & data mining , pages=

  139. [148]

    Management Science , volume=

    Are consumers strategic? Structural estimation from the air-travel industry , author=. Management Science , volume=. 2014 , publisher=

    2014

  140. [149]

    Manufacturing & Service Operations Management , volume=

    A data-driven approach to personalized bundle pricing and recommendation , author=. Manufacturing & Service Operations Management , volume=. 2020 , publisher=

    2020

  141. [150]

    Operations Research , volume=

    Data-driven pricing for a new product , author=. Operations Research , volume=. 2022 , publisher=

    2022

  142. [151]

    Management Science , volume=

    Feature-based dynamic pricing , author=. Management Science , volume=. 2020 , publisher=

    2020

  143. [152]

    Surveys in operations research and management science , volume=

    Dynamic pricing and learning: Historical origins, current research, and new directions , author=. Surveys in operations research and management science , volume=. 2015 , publisher=

    2015

  144. [153]

    Journal of Revenue and Pricing Management , volume=

    Dynamic pricing of airline offers , author=. Journal of Revenue and Pricing Management , volume=. 2018 , publisher=

    2018

  145. [154]

    INFORMS Journal on Data Science , volume=

    Causal decision making and causal effect estimation are not the same… and why it matters , author=. INFORMS Journal on Data Science , volume=. 2022 , publisher=

    2022

  146. [155]

    outcome prediction , author=

    Causal classification: Treatment effect estimation vs. outcome prediction , author=. Journal of Machine Learning Research , volume=

  147. [156]

    1999 , publisher=

    Integer and combinatorial optimization , author=. 1999 , publisher=

    1999

  148. [157]

    Journal of heuristics , volume=

    A combined Lagrangian, linear programming, and implication heuristic for large-scale set partitioning problems , author=. Journal of heuristics , volume=. 1996 , publisher=

    1996

  149. [158]

    Operations research , volume=

    Branch-and-price: Column generation for solving huge integer programs , author=. Operations research , volume=. 1998 , publisher=

    1998

  150. [159]

    2006 , publisher=

    Column generation , author=. 2006 , publisher=

    2006

  151. [160]

    Transportation Science , volume=

    Dynamic column generation for dynamic vehicle routing with time windows , author=. Transportation Science , volume=. 2006 , publisher=

    2006

  152. [161]

    INFORMS Journal on Computing , volume=

    An effective deflected subgradient optimization scheme for implementing column generation for large-scale airline crew scheduling problems , author=. INFORMS Journal on Computing , volume=. 2008 , publisher=

    2008

  153. [162]

    Operations research , volume=

    A column generation algorithm for choice-based network revenue management , author=. Operations research , volume=. 2009 , publisher=

    2009

  154. [163]

    International Conference on Machine Learning , pages=

    Counterfactual risk minimization: Learning from logged bandit feedback , author=. International Conference on Machine Learning , pages=

  155. [164]

    Transportation science , volume=

    Airline crew scheduling with time windows and plane-count constraints , author=. Transportation science , volume=. 2002 , publisher=

    2002

  156. [165]

    Mathematical Programming , volume=

    The volume algorithm: producing primal solutions with a subgradient method , author=. Mathematical Programming , volume=. 2000 , publisher=

    2000

  157. [166]

    Networks , volume=

    A survey of resource constrained shortest path problems: Exact solution approaches , author=. Networks , volume=. 2013 , publisher=

    2013

  158. [167]

    Optimization in Large Scale Problems , pages=

    Solving Large Scale Optimization Problems in the Transportation Industry and Beyond Through Column Generation , author=. Optimization in Large Scale Problems , pages=. 2019 , publisher=

    2019

  159. [168]

    Column generation , pages=

    Shortest path problems with resource constraints , author=. Column generation , pages=. 2005 , publisher=

    2005

  160. [169]

    Decision Lists and Decision Trees

    F \"u rnkranz, Johannes. Decision Lists and Decision Trees. Encyclopedia of Machine Learning and Data Mining. 2017

    2017

  161. [170]

    Introducing IBM ILOG CPLEX Optimization Studio 20.1.0 , howpublished =

    CPLEX, IBM , year=. Introducing IBM ILOG CPLEX Optimization Studio 20.1.0 , howpublished =

  162. [171]

    INFORMS Journal on Optimization , volume=

    A practical price optimization approach for omnichannel retailing , author=. INFORMS Journal on Optimization , volume=. 2019 , publisher=

    2019

  163. [172]

    Long range planning , volume=

    Competing on the edge: Strategy as structured chaos , author=. Long range planning , volume=. 1998 , publisher=

    1998

  164. [173]

    Simple rules for a complex world , author=. HARv. Bus. REV , pages=

  165. [174]

    2021 , author =

    Demand Shaping Through Bundling and Product Configuration: A Dynamic Multiproduct Inventory-Pricing Model , journal =. 2021 , author =

    2021

  166. [175]

    2023 , author =

    Dynamic Pricing and Inventory Control of Substitutable Products in a Nonstationary Environment , journal =. 2023 , author =

    2023

  167. [176]

    2016 , author =

    Pricing Personalized Bundles: A New Approach and an Empirical Study , journal =. 2016 , author =

    2016

  168. [177]

    2021 , author =

    Adaptive Convex Clustering of Generalized Linear Models With Application in Purchase Likelihood Prediction , journal =. 2021 , author =

    2021

  169. [178]

    2008 , author =

    Dynamic Pricing and Resource Allocation Using Revenue Management for Multiservice Networks , journal =. 2008 , author =

    2008

  170. [179]

    2019 , publisher =

    Revenue Management and Pricing Analytics , author =. 2019 , publisher =

    2019

  171. [180]

    2015 , url=

    Upselling versus Upsetting Customers ? A Model of Intrinsic and Extrinsic Incentives ∗ , author=. 2015 , url=

    2015

  172. [181]

    Journal of Economic Literature , volume=

    Using synthetic controls: Feasibility, data requirements, and methodological aspects , author=. Journal of Economic Literature , volume=. 2021 , publisher=

    2021

  173. [182]

    Transportation Research Part A: Policy and Practice , volume=

    Estimating flight-level price elasticities using online airline data: A first step toward integrating pricing, demand, and revenue optimization , author=. Transportation Research Part A: Policy and Practice , volume=. 2014 , publisher=

    2014

  174. [183]

    Transportation Research Part A: Policy and Practice , volume=

    Accounting for price endogeneity in airline itinerary choice models: An application to Continental US markets , author=. Transportation Research Part A: Policy and Practice , volume=. 2017 , publisher=

    2017

  175. [184]

    1993 , publisher=

    Automobile prices in market equilibrium: Part I and II , author=. 1993 , publisher=

    1993

  176. [185]

    The antitrust revolution: economics, competition, and policy , volume=

    Rapid price communication and coordination: The airline tariff publishing case (1994) , author=. The antitrust revolution: economics, competition, and policy , volume=. 2004 , publisher=

    1994

  177. [186]

    2011 , publisher=

    Linear programming and network flows , author=. 2011 , publisher=

    2011

  178. [187]

    INFORMS Journal on Computing , year=

    Constraint Learning to Define Trust Regions in Optimization over Pre-Trained Predictive Models , author=. INFORMS Journal on Computing , year=

  179. [188]

    American journal of epidemiology , volume=

    Doubly robust estimation of causal effects , author=. American journal of epidemiology , volume=. 2011 , publisher=

    2011

  180. [189]

    Biometrics , volume=

    Doubly robust estimation in missing data and causal inference models , author=. Biometrics , volume=. 2005 , publisher=

    2005

  181. [190]

    2014 , publisher=

    Segmentation, revenue management and pricing analytics , author=. 2014 , publisher=

    2014

  182. [191]

    Advances in Neural Information Processing Systems , volume=

    Fair and optimal decision trees: A dynamic programming approach , author=. Advances in Neural Information Processing Systems , volume=

  183. [192]

    International Conference on Machine Learning , pages=

    Generalized and scalable optimal sparse decision trees , author=. International Conference on Machine Learning , pages=

  184. [193]

    Advances in Neural Information Processing Systems , volume=

    Optimal sparse decision trees , author=. Advances in Neural Information Processing Systems , volume=

  185. [194]

    Symbolic Systems Distinguished Speaker , volume=

    The AI alignment problem: why it is hard, and where to start , author=. Symbolic Systems Distinguished Speaker , volume=

  186. [195]

    Management Science , volume=

    Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity , author=. Management Science , volume=. 2021 , publisher=

    2021

  187. [196]

    Decision Support Systems , volume=

    Deep learning based personalized recommendation with multi-view information integration , author=. Decision Support Systems , volume=. 2019 , publisher=

    2019

  188. [197]

    Manufacturing & Service Operations Management , volume=

    Market segmentation trees , author=. Manufacturing & Service Operations Management , volume=. 2023 , publisher=

    2023

  189. [198]

    International Journal of Contemporary Economics and Administrative Sciences , volume=

    Customer segmentation by using RFM model and clustering methods: a case study in retail industry , author=. International Journal of Contemporary Economics and Administrative Sciences , volume=

  190. [199]

    2024 , eprint=

    The piranha problem: Large effects swimming in a small pond , author=. 2024 , eprint=

    2024

  191. [200]

    Indiana Law Journal, Forthcoming , year=

    Regulating Healthcare Coverage Algorithms , author=. Indiana Law Journal, Forthcoming , year=

  192. [201]

    Frontiers in Artificial Intelligence , volume=

    Decision trees: from efficient prediction to responsible AI , author=. Frontiers in Artificial Intelligence , volume=. 2023 , publisher=

    2023

  193. [202]

    Journal of Research in Interactive Marketing , year=

    Not all sunshine and rainbows: exploring the dark side of AI in interactive marketing , author=. Journal of Research in Interactive Marketing , year=

  194. [203]

    Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V

    Practical Contextual Bandits for Large-Scale Structured Discrete Constrained Optimization Problems , author=. Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 , pages=

  195. [204]

    2018 , publisher =

    Microeconomics , author =. 2018 , publisher =

    2018

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.