Abstract
Determining causal effects of interventions onto outcomes from observational (non-randomized) data, e.g., treatment repurposing using electronic health records, is challenging due to underlying bias. Causal deep learning has improved over traditional techniques for estimating individualized treatment effects. We present the Doubly Robust Variational Information-theoretic Deep Adversarial Learning (DR-VIDAL), a novel generative framework that combines two joint models of treatment and outcome, ensuring an unbiased estimation even when one of the two is misspecified. DR-VIDAL uses a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; then, an information-theoretic generative adversarial network (Info-GAN) is used to generate counterfactuals; finally, a doubly robust block incorporates propensity matching/weighting into predictions. On synthetic and real-world datasets, DR-VIDAL achieves better performance than other non-generative and generative methods. In conclusion, DR-VIDAL uniquely fuses causal assumptions, VAE, Info-GAN, and doubly robustness into a comprehensive, performant framework. Code is available at: https://bitbucket.org/goingdeep2406/dr-vidal/src/master/