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：从真实世界观察性（非随机化）数据（例如利用电子健康记录进行药物重定位）中确定干预措施对结果的影响效应，由于存在潜在偏差而颇具挑战。因果深度学习已在估计个体化治疗效果方面相较于传统技术有所改进。本文提出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，结合了治疗与结果的两个联合模型，确保即使其中一个模型设定有误也能实现无偏的ITE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素分解为潜在变量；(ii) 一个信息论生成对抗网络，用于生成反事实样本；(iii) 一个整合治疗倾向性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（婴儿健康与发展项目、双胞胎出生登记、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性能卓越的框架。代码可在MIT许可下获取：https://github.com/Shantanu48114860/DR-VIDAL-AMIA-22。