Counterfactual estimation over time is important in various applications, such as personalized medicine. However, time-dependent confounding bias in observational data still poses a significant challenge in achieving accurate and efficient estimation. We introduce causal autoencoding and treatment conditioning (CAETC), a novel method for this problem. Built on adversarial representation learning, our method leverages an autoencoding architecture to learn a partially invertible and treatment-invariant representation, where the outcome prediction task is cast as applying a treatment-specific conditioning on the representation. Our design is independent of the underlying sequence model and can be applied to existing architectures such as long short-term memories (LSTMs) or temporal convolution networks (TCNs). We conduct extensive experiments on synthetic, semi-synthetic, and real-world data to demonstrate that CAETC yields significant improvement in counterfactual estimation over existing methods.
@article{arxiv.2603.11565,
title = {CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Time},
author = {Nghia D. Nguyen and Pablo Robles-Granda and Lav R. Varshney},
journal= {arXiv preprint arXiv:2603.11565},
year = {2026}
}