Variational Auto-encoders for Causal Inference
Abstract: Estimating causal effects from observational data (at either an individual- or a population- level) is critical for making many types of decisions. One approach to address this task is to learn decomposed representations of the underlying factors of data; this becomes significantly more challenging when there are confounding factors (which influence both the cause and the effect). In this paper, we take a generative approach that builds on the recent advances in Variational Auto-Encoders to simultaneously learn those underlying factors as well as the causal effects. We propose a progressive sequence of models, where each improves over the previous one, culminating in the Hybrid model. Our empirical results demonstrate that the performance of the proposed hybrid models are superior to both state-of-the-art discriminative as well as other generative approaches in the literature.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Andrew_Miller1
Submission Number: 296
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