Estimating treatment effects over time is relevant in many real-world applications, such as precision medicine, epidemiology, economy, and marketing. Many state-of-the-art methods either assume the observations of all confounders or seek to infer the unobserved ones. We take a different perspective by assuming unobserved risk factors, i.e., adjustment variables that affect only the sequence of outcomes. Under unconfoundedness, we target the Individual Treatment Effect (ITE) estimation with unobserved heterogeneity in the treatment response due to missing risk factors. We address the challenges posed by time-varying effects and unobserved adjustment variables. Led by theoretical results over the validity of the learned adjustment variables and generalization bounds over the treatment effect, we devise Causal DVAE (CDVAE). This model combines a Dynamic Variational Autoencoder (DVAE) framework with a weighting strategy using propensity scores to estimate counterfactual responses. The CDVAE model allows for accurate estimation of ITE and captures the underlying heterogeneity in longitudinal data. Evaluations of our model show superior performance over state-of-the-art models.

翻译：在诸多真实应用场景（如精准医学、流行病学、经济学和市场营销）中，随时间变化的治疗效果估计具有重要价值。现有最先进方法或假定所有混杂因素可观测，或试图推断未观测混杂因素。我们另辟蹊径，假设存在未观测的风险因子，即仅影响结果序列的调整变量。在无混杂性假设下，我们针对因缺失风险因子导致的个体治疗效果（ITE）估计中的未观测异质性展开研究，并应对时变效应与未观测调整变量带来的双重挑战。基于对所学调整变量有效性的理论论证以及对治疗效果泛化界的研究，我们提出了因果DVAE（CDVAE）模型。该模型将动态变分自编码器（DVAE）框架与基于倾向得分的加权策略相结合以估计反事实响应。CDVAE模型不仅能精确估计ITE，还能捕捉纵向数据中的潜在异质性。实验评估表明，该模型性能优于现有最先进方法。