arXiv Machine Learning June 8, 2026 By Aparajithan Venkateswaran, Anirudh Sankar, Arun G. Chandrasekhar, Tyler H. McCormick

Robustly estimating heterogeneity in factorial data using Rashomon Partitions

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arXiv:2404. 02141v5 Announce Type: replace-cross Abstract: In both observational data and randomized control trials, researchers select statistical models to articulate how the outcome of interest varies with combinations of observable covariates.

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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.

By Wenlong Ji, Lihua Lei, Asher Spector

arXiv Machine Learning

Jul 17

Causal Inference for Sequential Settings under Interference and Latent Confounding

arXiv:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.

By Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah

arXiv Machine Learning

Jun 2

Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference

arXiv:2603. 12037v2 Announce Type: replace Abstract: Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an in-context learning problem.

By Valentyn Melnychuk, Vahid Balazadeh, Stefan Feuerriegel, Rahul G. Krishnan

llms safety

arXiv AI

Jun 12

Is It You or Your Environment? A Bayesian Inference Framework for Genomically-Anchored Personalized Physiological Interpretation

arXiv:2606. 13556v1 Announce Type: new Abstract: Personalized health AI systems face a fundamental cold-start problem: machine learning models for physiological interpretation require weeks of individual behavioral data before they can distinguish constitutional variation from environmentally driven deviation.

By Aruna Dey, Suraj Biswas

Hugging Face Trending Papers

Sep 21

Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models

Hierarchical data is ubiquitous in the empirical sciences and is most commonly analyzed with generalized linear mixed-effects models (GLMMs). Bayesian inference for GLMMs yields calibrated uncertainty...

llms benchmarks

arXiv AI

Jun 17

Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference

arXiv:2606. 17165v1 Announce Type: cross Abstract: Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost.

By Joel Persson, M{\aa}rten Schultzberg, Sebastian Ankargren