3 papers
stat.ME2026
Proximal Causal Inference for Conditional Separable Effects
Chan Park, Mats Stensrud, Eric Tchetgen Tchetgen
Scientists regularly pose questions about treatment effects on outcomes conditional on a post-treatment event. However, causal inference in such settings requires care, even in per…
stat.ME2026
Distributional Balancing for Causal Inference: A Unified Framework via Characteristic Function Distance
Diptanil Santra, Guanhua Chen, Chan Park
Weighting methods are essential tools for estimating causal effects in observational studies, with the goal of balancing pre-treatment covariates across treatment groups. Tradition…
stat.ME2026
Nonparametric Inference with an Instrumental Variable under a Separable Binary Treatment Choice Model
Chan Park, Eric Tchetgen Tchetgen
Instrumental variable (IV) methods are widely used to infer treatment effects in the presence of unmeasured confounding. In this paper, we study nonparametric inference with an IV…