arXiv Machine Learning August 24, 2026 By Marlies Hafer, Alexander Marx

Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

safety

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.

By Yiwen Qiu, Filip Kova\v{c}evi\'c, Shimeng Huang, Peter Spirtes, Francesco Locatello

safety

arXiv Machine Learning

Jul 1

Coarsening Bias from Variable Discretization in Causal Functionals

arXiv:2602. 22083v2 Announce Type: replace-cross Abstract: Causal identification functionals often require integration over conditional densities of continuous variables, such as those arising in nonparametric identification theory of total and mediated causal effects in DAGs with hidden variables.

By Xiaxian Ou, Razieh Nabi

safety

arXiv Statistics ML

1d ago

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.

By Wenlong Ji, Lihua Lei, Asher Spector

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.

By Yichen Xu

llms diffusion benchmarks safety

arXiv Machine Learning

Jul 7

Two Layers of Instability in Causal Estimation

arXiv:2606. 21185v2 Announce Type: replace-cross Abstract: There is a precise sense in which drawing causal inferences from observational data is hard, even when identifiability is assumed.

By Alexis Bellot

multimodal

arXiv Machine Learning

Aug 4

Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference

arXiv:2608. 01352v1 Announce Type: new Abstract: Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference.

By Omar Faruque, Pavan Raj Ravi, Jianwu Wang

llms

One email a morning, machine-written

Related stories

Towards a holistic understanding of Selection Bias for Causal Effect Identification

Coarsening Bias from Variable Discretization in Causal Functionals

Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects

Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities

Two Layers of Instability in Causal Estimation

Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference