提出新方法提升时间序列反事实估计准确性，尤其适用于医疗个性化决策。
CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Time
- 用因果自编码与治疗条件化学习不变表示，缓解时间混杂偏误。
- 在合成、半合成及真实数据上显著优于现有方法，提升反事实预测精度。
- 可兼容LSTM、TCN等模型，适合医疗、金融等领域时序决策场景。
时间序列上的反事实估计在个性化医疗等应用中至关重要，但观察数据中的时间依赖性混杂偏差仍是准确高效估计的主要挑战。本文提出因果自编码与治疗条件化（CAETC）方法，基于对抗性表征学习，采用自编码架构学习部分可逆且治疗无关的表征，将结果预测任务建模为对表征施加治疗特定条件。该设计不依赖底层序列模型，可集成至长短期记忆网络（LSTMs）或时序卷积网络（TCNs）等现有架构。通过在合成、半合成及真实世界数据上的广泛实验，验证了CAETC在反事实估计上显著优于现有方法。
原文摘要 · Abstract (English)
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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