Variational Auto-Encoder Architectures that Excel at Causal Inference
This addresses the challenge of causal inference with confounding factors, which is critical for decision-making in fields like healthcare or economics, though it appears incremental as it builds on existing VAE methods.
The paper tackles the problem of estimating causal effects from observational data by proposing a progressive sequence of generative models based on Variational Auto-Encoders to learn underlying factors and causal effects simultaneously, with empirical results showing superior performance over state-of-the-art discriminative and generative approaches.
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 all three proposed models are superior to both state-of-the-art discriminative as well as other generative approaches in the literature.