Determining causal effects of interventions onto outcomes from real-world, 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 (ITE). 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 ITE estimation even when one of the two is misspecified. DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii) an information-theoretic generative adversarial network (Info-GAN) to generate counterfactuals; (iii) a doubly robust block incorporating treatment propensities for outcome predictions. On synthetic and real-world datasets (Infant Health and Development Program, Twin Birth Registry, and National Supported Work Program), 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://github.com/Shantanu48114860/DR-VIDAL-AMIA-22 under MIT license.

翻译：从真实世界的观测性（非随机化）数据（例如，利用电子健康记录进行药物重定位）中确定干预对结果的因果效应，由于存在潜在偏倚，具有挑战性。因果深度学习在估计个体化处理效应方面已优于传统技术。我们提出了双重稳健变分信息论深度对抗学习（DR-VIDAL），这是一种新颖的生成式框架，它结合了处理与结果的两个联合模型，确保即使其中一个模型设定错误，仍能得到无偏的个体化处理效应估计。DR-VIDAL整合了：（i）一个变分自编码器，用于根据因果假设将混杂因子分解为潜变量；（ii）一个信息论生成对抗网络，用于生成反事实；（iii）一个包含处理倾向性以进行结果预测的双重稳健模块。在合成数据集和真实世界数据集（婴儿健康与发展计划、双胞胎出生登记处、国家支持工作计划）上，DR-VIDAL取得了优于其他非生成式和生成式方法的性能。总之，DR-VIDAL独特地将因果假设、VAE、Info-GAN和双重稳健性融合为一个全面且高性能的框架。代码可在 MIT 许可下于 https://github.com/Shantanu48114860/DR-VIDAL-AMIA-22 获取。