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Determining causal effects of interventions onto outcomes from real-world,observational (non-randomized) data, e.g., treatment repurposing usingelectronic health records, is challenging due to underlying bias. Causal deeplearning has improved over traditional techniques for estimating individualizedtreatment effects (ITE). We present the Doubly Robust VariationalInformation-theoretic Deep Adversarial Learning (DR-VIDAL), a novel generativeframework that combines two joint models of treatment and outcome, ensuring anunbiased ITE estimation even when one of the two is misspecified. DR-VIDALintegrates: (i) a variational autoencoder (VAE) to factorize confounders intolatent variables according to causal assumptions; (ii) an information-theoreticgenerative adversarial network (Info-GAN) to generate counterfactuals; (iii) adoubly robust block incorporating treatment propensities for outcomepredictions. On synthetic and real-world datasets (Infant Health andDevelopment Program, Twin Birth Registry, and National Supported Work Program),DR-VIDAL achieves better performance than other non-generative and generativemethods. In conclusion, DR-VIDAL uniquely fuses causal assumptions, VAE,Info-GAN, and doubly robustness into a comprehensive, performant framework.Code is available at: https://github.com/Shantanu48114860/DR-VIDAL-AMIA-22under MIT license.

DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep  Adversarial Learning for Counterfactual Prediction and Treatment Effect  Estimation on Real World Data