Variational Causal Inference

TL;DR

This paper introduces a deep variational Bayesian method for causal inference that effectively combines individual high-dimensional outcomes and response distributions of similar subjects to estimate counterfactual outcomes.

Contribution

It presents a novel framework that leverages both factual outcomes and similar subjects' response distributions for high-dimensional causal inference.

Findings

Improved accuracy in estimating counterfactual outcomes for high-dimensional data

Effective integration of individual outcomes and similar subjects' response distributions

Demonstrated robustness on complex, high-dimensional datasets

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.