Accurately estimating treatment effects over time is crucial in fields such as precision medicine, epidemiology, economics, and marketing. Many current methods for estimating treatment effects over time assume that all confounders are observed or attempt to infer unobserved ones. In contrast, our approach focuses on unobserved adjustment variables, which specifically have a causal effect on the outcome sequence. Under the assumption of unconfoundedness, we address estimating Conditional Average Treatment Effects (CATEs) while accounting for unobserved heterogeneity in response to treatment due to these unobserved adjustment variables. Our proposed Causal Dynamic Variational Autoencoder (CDVAE) is grounded in theoretical guarantees concerning the validity of latent adjustment variables and generalization bounds on CATE estimation error. Extensive evaluations on synthetic and real-world datasets show that CDVAE outperforms existing baselines. Moreover, we demonstrate that state-of-the-art models significantly improve their CATE estimates when augmented with the latent substitutes learned by CDVAE, approaching oracle-level performance without direct access to the true adjustment variables.

翻译：在精准医学、流行病学、经济学和市场营销等领域，准确估计随时间变化的处理效应至关重要。当前许多估计时序处理效应的方法假设所有混杂变量均已观测，或试图推断未观测的混杂变量。相比之下，我们的方法聚焦于未观测的调整变量——这些变量专门对结果序列产生因果效应。在无混杂性假设下，我们致力于估计条件平均处理效应（CATE），同时考虑因这些未观测调整变量导致的处理响应中的未观测异质性。我们提出的因果动态变分自编码器（CDVAE）基于以下理论保证：潜在调整变量的有效性以及CATE估计误差的泛化界。在合成和真实数据集上的广泛评估表明，CDVAE优于现有基线方法。此外，我们证明当先进模型与CDVAE学习到的潜在替代变量结合时，其CATE估计显著提升，在无法直接获取真实调整变量的情况下接近理论最优性能。