arXiv Machine Learning
Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes
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arXiv Machine Learning
Jul 10
Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment
arXiv:2603. 19186v3 Announce Type: replace Abstract: Randomized controlled trials (RCTs) are the gold standard for estimating treatment effects, yet they are often underpowered for detecting effect heterogeneity.
arXiv AI
Jul 29
Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records
arXiv:2507. 20993v4 Announce Type: replace-cross Abstract: We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text.
arXiv AI
Jul 17
Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
arXiv:2604. 23904v3 Announce Type: replace-cross Abstract: Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference.
arXiv Statistics ML
2d ago
Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference
arXiv:2609.17238v1 Announce Type: cross Abstract: High-dimensional data create challenges for causal effect estimation because identifying the covariates needed for correct model specification become...
arXiv Statistics ML
Aug 25
Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects
The paper introduces a model‑agnostic inference framework for partially identified causal effects that leverages covariate information without requiring discrete covariates or accurate conditional distribution estimates. Using duality theory for optimal transport, the method delivers uniformly valid inference in randomized experiments, is doubly robust in observational settings, achieves asymptotic unbiasedness when nuisance parameters converge semiparametrically, and allows multiplier‑bootstrap selection of covariates and models while remaining computationally efficient. Empirical applications show the approach consistently narrows identified sets and confidence intervals without imposing extra structural assumptions.
arXiv Machine Learning
Jul 27
LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies
arXiv:2604. 16763v3 Announce Type: replace Abstract: Causal inference from electronic health records (EHR) is fundamentally limited by unmeasured confounding: critical clinical states such as frailty, goals of care, and mental status are documented in free-text notes but absent from structured data.