arXiv Statistics ML
Bridging Local and Population Causal Effects: A Proximal Instrumental Variable Approach
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arXiv Statistics ML
Aug 24
Double Machine Learning of Continuous Treatment Effects with Additive Instrumental Variables
The paper introduces a new framework for identifying average dose-response functions in the presence of unmeasured confounding by using instrumental variables. It defines a uniform regular weighting function and partitions the treatment space into open sets where local identification is possible. For estimation, the authors propose an augmented inverse probability weighted score within a debiased machine learning setting, along with practical guidance for constructing weighting functions, falsification tests for the additive IV condition, and asymptotic theory for kernel regression or empirical risk minimization estimators.
arXiv AI
Jun 2
Towards a holistic understanding of Selection Bias for Causal Effect Identification
arXiv:2605. 13430v3 Announce Type: replace-cross Abstract: Selection bias is pervasive in observational studies.
arXiv Statistics ML
Aug 21
Causal Generalization of Continuous Treatment Effects under Covariate Shift
arXiv:2608. 19383v1 Announce Type: cross Abstract: Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population.
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 Machine Learning
Jun 8
Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions
arXiv:2606. 07399v1 Announce Type: cross Abstract: Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nuisance model misspecification.
arXiv Machine Learning
Sep 23
Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
The paper introduces a new estimand for conditional distributional treatment effects that captures how treatments influence the entire outcome distribution, including variance and tail risks, in a covariate-dependent manner. It presents a doubly robust estimator that is minimax optimal locally and uses it to construct a test for global homogeneity of conditional potential outcome distributions. The test accommodates discrepancies beyond the maximum mean discrepancy, guarantees valid type‑1 error, is consistent against fixed alternatives, and includes a computationally efficient, permutation‑free algorithm with exact closed‑form expressions for two natural discrepancies.