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：确定干预措施对现实世界、观察(非随机化)数据结果的因果关系,例如利用电子健康记录进行治疗的重新标定,由于潜在的偏差而具有挑战性; 与评估个人化治疗效果的传统技术相比,由于原因的深层次学习有所改进(ITE); 我们介绍了Doubly Robust Varial Inforational Information-theoretic Deep Aversarial Learning(DR-VIDAL),这是一个新颖的基因化框架,将两种治疗和结果的联合模式结合起来,确保即使其中之一被错误地描述,也进行公正的ITE估计。 DR-VIDAL整合:(一) 变式自动自动计算器(VAE),根据因果假设将混杂者纳入潜在变变变变量;(二) 信息理论式对抗网络(Info-GAN) 产生反事实;(三) 一个将结果预测的治疗倾向性极强的硬块。</s>