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New arXiv papers tackle causal inference with selection bias and confounding

By PulseAugur Editorial · [2 sources] ·

Two new research papers on arXiv explore advanced methods for causal inference in complex scenarios. The first paper introduces a model-agnostic approach using optimal transport duality to provide valid inference for partially identified causal effects, even with inaccurate initial estimates. The second paper proposes a regression-based estimator to address selection bias and confounding in causal effect estimation, utilizing proxy variables to improve accuracy and reduce variance. AI

IMPACT These papers advance statistical methodologies relevant to AI research, particularly in understanding causal relationships within complex datasets.

RANK_REASON Two academic papers published on arXiv detailing new statistical methods.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New arXiv papers tackle causal inference with selection bias and confounding

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Two academic papers published on arXiv detailing new statistical methods.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Wenlong Ji, Lihua Lei, Asher Spector ·

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

    arXiv:2310.08115v3 Announce Type: replace-cross Abstract: Many causal estimands are only partially identifiable since they depend on the unobservable joint distribution between potential outcomes. Stratification on pretreatment covariates can yield sharper bounds; however, unless…

  2. arXiv stat.ML TIER_1 English(EN) · Marlies Hafer, Alexander Marx ·

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

    arXiv:2503.20546v2 Announce Type: replace 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. In settings without selection bias or co…

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