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

• URL: http://arxiv.org/abs/2603.11565v1
• Date: Thu, 12 Mar 2026 05:37:56 GMT
• Title: CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Time
• Abstract summary: We introduce causal autoencoding and treatment conditioning (CAETC), a novel method for this problem.<n>Built on adversarial representation learning, our method leverages an autoencoding architecture to learn a partially invertible and treatment-invariant representation.<n>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.
• Score: 11.100922246022343
• License: http://creativecommons.org/licenses/by/4.0/
• 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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