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
arXiv Machine Learning
Jul 30

From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Data

arXiv:2607. 26521v1 Announce Type: new Abstract: Conventional subgroup analyses can yield unstable and difficult-to-interpret conclusions, especially in observational biomedical data where each individual is observed under only one exposure state, true individual treatment effects are unavailable, and causal structure is uncertain.

By Vasundhara Acharya, Bulent Yener