用熵惩罚机制提升时间序列反事实推理的准确性。
CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
- 基于变分与对抗自编码器，引入条件熵惩罚增强潜在表示解耦。
- 在合成、半合成及真实数据上均优于现有方法，尤其在时序反事实预测中表现更优。
- 适合金融、医疗等领域需分析事件影响的场景，模型可解释性强。
准确进行时间序列反事实推断对金融、医疗和营销等领域的决策至关重要，有助于理解事件或治疗对结果随时间的影响。本文针对受市场事件影响的时间序列数据，提出一种新型反事实推断方法，源于工业应用场景。基于因果推断中的溯因-行动-预测流程与结构因果模型框架，我们首先将变分自编码器和对抗自编码器方法扩展至时间序列领域（此前未被用于此类数据）。随后，提出条件熵惩罚自编码器（CEPAE），通过在潜在空间施加熵惩罚损失，促使数据表示解耦。我们在合成、半合成及真实世界数据集上从理论和实验两方面验证该方法，结果表明，CEPAE在所评估指标上普遍优于其他方法。
原文摘要 · Abstract (English)
The ability to accurately perform counterfactual inference on time series is crucial for decision-making in fields like finance, healthcare, and marketing, as it allows us to understand the impact of events or treatments on outcomes over time. In this paper, we introduce a new counterfactual inference approach tailored to time series data impacted by market events, which is motivated by an industrial application. Utilizing the abduction-action-prediction procedure and the Structural Causal Model framework, we first adapt methods based on variational autoencoders and adversarial autoencoders, both previously used in counterfactual literature although not in time series settings. Then, we present the Conditional Entropy-Penalized Autoencoder (CEPAE), a novel autoencoder-based approach for counterfactual inference, which employs an entropy penalization loss over the latent space to encourage disentangled data representations. We validate our approach both theoretically and experimentally on synthetic, semi-synthetic, and real-world datasets, showing that CEPAE generally outperforms the other approaches in the evaluated metrics.
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