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arXiv · 2603.11565

CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Time

Nghia D. Nguyen·Pablo Robles-Granda·Lav R. Varshney
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Abstract

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.

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Nghia D. Nguyen, Pablo Robles-Granda, Lav R. Varshney. 2026-03-12. CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Time. https://arxiv.org/abs/2603.11565

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