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arXiv Statistics ML

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.

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arXiv Statistics ML
Oct 5

Flexible Nonparametric Inference for Causal Effects under the Front-Door Model

The paper introduces new one‑step and targeted minimum loss‑based estimators for the average treatment effect and the average treatment effect on the treated under the front‑door criterion. These estimators use multiple parameterizations of the observed data distribution, can avoid modeling the mediator density, and are compatible with flexible machine‑learning nuisance estimation. The authors derive conditions for root‑n consistency, asymptotic linearity, and develop doubly robust tests for identification assumptions, demonstrating improved efficiency and favorable performance in simulations and real‑world applications in education and emergency medicine.

By Anna Guo, David Benkeser, Razieh Nabi
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arXiv Machine Learning
Oct 7

ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding

ProximalFM is a transformer‑based model that uses prior‑data fitted networks (PFNs) to perform Bayesian proximal causal inference under hidden confounding. By training on synthetic data generated from structural causal models with oracle counterfactuals, it amortizes the Bayesian operator inversion into a single forward pass, producing posterior estimates of the conditional average treatment effect (CATE). The approach consistently outperforms prior methods across various proximal regimes, especially when latent confounding is strong and proxy variables are weakly informative, and it requires no dataset‑specific tuning.

By Christophe Muller, Ayub Kharel, Alex Luedtke, Chan Park, Eric Tchetgen Tchetgen, Juan L. Gamella, Rahul Krishnan, Ricardo Silva, Jakob Zeitler
llms
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arXiv Machine Learning
Oct 7

Assumption-lean logistic regression with missing covariates

arXiv:2610.07292v1 Announce Type: cross Abstract: Missing covariates are frequently encountered in supervised learning problems, and classical methods for estimation using such data use carefully cho...

By Jyotishka Ray Choudhury, Kabir Aladin Verchand, Richard J. Samworth, Ashwin Pananjady
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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.

By Saksham Jain, Alex Luedtke
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arXiv Statistics ML
Sep 22

Doubly robust target inference for generalized linear regression with completely missing covariates

arXiv:2609.24086v1 Announce Type: cross Abstract: Large-scale multipurpose cohort studies and biobanks often omit covariates needed for specific downstream analyses. We study target-population infere...

By Huali Zhao (School of Mathematics and Statistics, Huazhong University of Science and Technology), Ke Deng (Department of Statistics and Data Science, Tsinghua University)
fine-tuning
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