Abstract
Modern data analysis frequently relies on the use of large datasets, often constructed as amalgamations of diverse populations or data-sources. Heterogeneity across these smaller datasets constitutes two major challenges for causal inference; (1) the source of each sample can introduce latent confounding between treatment and effect, and (2) diverse populations may respond differently to the same treatment, giving rise to heterogeneous treatment effects (HTEs). The issues of latent confounding and HTEs have been studied separately but not in conjunction. In particular, previous works only report the conditional average treatment effect (CATE) among similar individuals (with respect to the measured covariates). CATEs cannot resolve mixtures of potential treatment effects driven by latent heterogeneity, which we call mixtures of treatment effects (MTEs). Inspired by method of moment approaches to mixture models, we propose “synthetic potential outcomes” (SPOs). Our new approach deconfounds heterogeneity while also guaranteeing the identifiability of MTEs. This technique bypasses full recovery of a mixture, which significantly simplifies its requirements for identifiability. We demonstrate the efficacy of SPOs on synthetic data.
Bijan Mazaheri
Postdoctoral Associate
My interests include mixture models, high level data fusion, and stability to distribution shift - usually through the lense of causality.