Variational Causal Inference
Abstract
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, impulse responses, human faces) and covariates are relatively limited. In this case, to construct one's outcome under a counterfactual treatment, it is crucial to leverage individual information contained in its observed factual outcome on top of the covariates. We propose a deep variational Bayesian framework that rigorously integrates two main sources of information for outcome construction under a counterfactual treatment: one source is the individual features embedded in the high-dimensional factual outcome; the other source is the response distribution of similar subjects (subjects with the same covariates) that factually received this treatment of interest.
Cite
@article{arxiv.2209.05935,
title = {Variational Causal Inference},
author = {Yulun Wu and Layne C. Price and Zichen Wang and Vassilis N. Ioannidis and Robert A. Barton and George Karypis},
journal= {arXiv preprint arXiv:2209.05935},
year = {2025}
}