Variational Causal Inference
This addresses a key problem in causal inference for fields like genomics and image analysis where outcomes are high-dimensional and covariates are limited.
The paper tackles the challenge of estimating high-dimensional potential outcomes under counterfactual treatments by leveraging individual factual outcomes and similar subjects' response distributions, proposing a deep variational Bayesian framework to integrate these information sources.
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.