New study design elicits unobserved confounders by querying experts on treatment intent for matched units, with theoretical conditions and a proof-of-concept demonstration in ICU electronic health records using clinical notes as proxy.
In: The economics of artificial intelligence, 507–552
9 Pith papers cite this work, alongside 25 external citations. Polarity classification is still indexing.
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representative citing papers
Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
Presents a surrogacy framework for LLM-based A/B testing that identifies human average treatment effects through calibration under weaker conditions than full outcome equivalence.
NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent policy class comparisons.
Conformal inference produces robust prediction intervals for treatment effects under experimental attrition, outperforming complete-case, imputation, and weighting approaches in simulations.
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
SLV aggregates expected profit from current inventory through its full selling lifecycle to measure long-term opportunity costs within short A/B test windows.
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.
citing papers explorer
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Confounder Detection via Treatment Intent: A New Observational Study Design
New study design elicits unobserved confounders by querying experts on treatment intent for matched units, with theoretical conditions and a proof-of-concept demonstration in ICU electronic health records using clinical notes as proxy.
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The Statistical Cost of Adaptation in Multi-Source Transfer Learning
Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference
Presents a surrogacy framework for LLM-based A/B testing that identifies human average treatment effects through calibration under weaker conditions than full outcome equivalence.
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Nonparametric Bayesian Policy Learning
NBPL uses a nonparametric Dirichlet process prior on the reduced-form distribution for posterior inference on optimal treatment assignments and welfare, with minimax-optimal regret convergence and pointwise consistent policy class comparisons.
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Conformal Inference for Experimental Attrition in Social Science Research
Conformal inference produces robust prediction intervals for treatment effects under experimental attrition, outperforming complete-case, imputation, and weighting approaches in simulations.
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Assessing Estimate of CATE from Observational Data via an RCT Study
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
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Measuring Opportunity Cost with Stock Lifetime Value
SLV aggregates expected profit from current inventory through its full selling lifecycle to measure long-term opportunity costs within short A/B test windows.
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A Review of Causal Decision Making
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.
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