REVIEW 10 cited by
Towards optimal doubly robust estimation of heterogeneous causal effects
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Heterogeneous effect estimation plays a crucial role in causal inference, with applications across medicine and social science. Many methods for estimating conditional average treatment effects (CATEs) have been proposed in recent years, but there are important theoretical gaps in understanding if and when such methods are optimal. This is especially true when the CATE has nontrivial structure (e.g., smoothness or sparsity). Our work contributes in several main ways. First, we study a two-stage doubly robust CATE estimator and give a generic model-free error bound, which, despite its generality, yields sharper results than those in the current literature. We apply the bound to derive error rates in nonparametric models with smoothness or sparsity, and give sufficient conditions for oracle efficiency. Underlying our error bound is a general oracle inequality for regression with estimated or imputed outcomes, which is of independent interest; this is the second main contribution. The third contribution is aimed at understanding the fundamental statistical limits of CATE estimation. To that end, we propose and study a local polynomial adaptation of double-residual regression. We show that this estimator can be oracle efficient under even weaker conditions, if used with a specialized form of sample splitting and careful choices of tuning parameters. These are the weakest conditions currently found in the literature, and we conjecture that they are minimal in a minimax sense. We go on to give error bounds in the non-trivial regime where oracle rates cannot be achieved. Some finite-sample properties are explored with simulations.
Forward citations
Cited by 10 Pith papers
-
Debiased Machine Learning for Partially Linear Accelerated Failure Time Models
A debiased machine learning estimator for partially linear accelerated failure time models achieves valid inference on a target exposure under right censoring via an orthogonalized rank-based U-statistic and block-pai...
-
Causal Inference with Multiple Misclassified Exposures: A Control Variate-Adjusted Calibration Weighting Approach
New calibration weighting and control variate estimators for causal inference with multiple misclassified binary exposures achieve consistency and double robustness without modeling the misclassification process, with...
-
UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluation
UpliftBench finds that on IHDP Qini shows no detectable alignment with effect accuracy (+0.07 rank correlation, CI includes zero) while AUUC is consistently more aligned, and that on Jobs ranking metrics fail at sign-...
-
Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
A new meta-learner, the H-learner, interpolates between indirect and direct regularization for CATE estimation and shows small but consistent PEHE improvements on IHDP and ACIC 2016.
-
A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation
IWDD distills a pretrained conditional diffusion model into a one-step generator using randomized treatment sampling, implicitly reweighting observational data for confounding bias and reducing gradient variance.
-
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE
SimPONet jointly trains on real observational data and simulator counterfactuals to estimate CATE from post-treatment covariates, guided by a new generalization bound.
-
From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies
A causal framework for learning treatment policies with deferral, applied to diuretic dosing in acute heart failure with kidney injury.
-
Consistent Labeling Across Group Assignments: Variance Reduction in Conditional Average Treatment Effect Estimation
CLAGA re-labels each training instance with out-of-sample CATE estimates from K-fold primary models, eliminating group-assignment-dependent predictions and reducing PEHE on several benchmarks.
-
Shrinkage-Based Regressions with Many Related Treatments
A customized ridge regression with an unpenalized focal treatment effect produces lower-variance estimates for many sparse sub-treatments and exactly recovers the single-treatment estimator.
-
Exploring the heterogeneous impacts of Indonesia's conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests
Instrumental causal forests show that Indonesia's PKH cash transfer has heterogeneous effects on maternal health care use, with supply-side readiness and household poverty shaping who benefits and who does not.
Discussion (0). Continue with ORCID to comment.