arXiv Statistics ML
Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference
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arXiv AI
Aug 20
CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning
CausalProfiler is a synthetic benchmark generator designed to evaluate causal machine learning (Causal ML) methods more rigorously and transparently. It randomly samples causal models, data, queries, and ground truths based on explicit design choices across observation, intervention, and counterfactual reasoning levels, providing coverage guarantees and transparent assumptions. The authors demonstrate its utility by testing several state‑of‑the‑art methods under diverse conditions, both within and outside the identification regime, highlighting the insights CausalProfiler can reveal.
arXiv Machine Learning
Sep 10
Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes
arXiv:2609.06294v1 Announce Type: new Abstract: Estimating conditional average treatment effects (CATE) enables efficient targeting of interventions, but many applications have limited experimental s...
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
Aug 24
Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding
arXiv:2503. 20546v2 Announce Type: replace-cross Abstract: We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention, focusing on continuous random variables.
arXiv AI
Sep 2
When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation
arXiv:2609.00071v1 Announce Type: new Abstract: Prediction error is widely used to evaluate nuisance-function estimators in causal inference, but its relationship with causal estimator performance ma...
arXiv Machine Learning
Jun 18
Anti-causal domain generalization: Leveraging unlabeled data
arXiv:2602. 17187v2 Announce Type: replace-cross Abstract: The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments.