Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data
This addresses a key challenge in precision medicine, epidemiology, economics, and marketing by improving counterfactual regression in longitudinal data, though it is incremental as it builds on existing causal inference methods.
The paper tackled the problem of estimating treatment effects over time when unobserved adjustment variables affect outcomes, proposing a Causal Dynamic Variational Autoencoder (CDVAE) that outperformed existing baselines and enabled other models to approach oracle-level performance.
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.