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.

翻译：在金融、医疗和营销等领域，准确执行时间序列反事实推理的能力对于决策至关重要，因为它使我们能够理解事件或干预随时间推移对结果的影响。本文针对受市场事件影响的时间序列数据，提出一种新的反事实推理方法，其灵感来源于一项工业应用。利用溯因-行动-预测流程和结构因果模型框架，我们首先对基于变分自编码器和对抗自编码器的方法进行了适应性改进——这两种方法虽在反事实文献中已有应用，但尚未用于时间序列场景。随后，我们提出了条件熵惩罚自编码器（CEPAE），这是一种基于自编码器的新型反事实推理方法，通过在潜在空间上施加熵惩罚损失来促进解耦的数据表示。我们在合成、半合成和真实数据集上从理论和实验两方面验证了所提方法，结果表明CEPAE在评估指标上通常优于其他方法。