Estimating treatment effects over time holds significance in various domains, including precision medicine, epidemiology, economy, and marketing. This paper introduces a unique approach to counterfactual regression over time, emphasizing long-term predictions. Distinguishing itself from existing models like Causal Transformer, our approach highlights the efficacy of employing RNNs for long-term forecasting, complemented by Contrastive Predictive Coding (CPC) and Information Maximization (InfoMax). Emphasizing efficiency, we avoid the need for computationally expensive transformers. Leveraging CPC, our method captures long-term dependencies in the presence of time-varying confounders. Notably, recent models have disregarded the importance of invertible representation, compromising identification assumptions. To remedy this, we employ the InfoMax principle, maximizing a lower bound of mutual information between sequence data and its representation. Our method achieves state-of-the-art counterfactual estimation results using both synthetic and real-world data, marking the pioneering incorporation of Contrastive Predictive Encoding in causal inference.

翻译：时序治疗效果估计在精准医疗、流行病学、经济学和市场营销等多个领域具有重要意义。本文提出了一种独特的时序反事实回归方法，重点在于长期预测。与Causal Transformer等现有模型不同，我们的方法凸显了循环神经网络在长期预测中的有效性，并结合了对比预测编码与信息最大化原则。我们强调计算效率，避免使用计算成本高昂的Transformer架构。通过利用对比预测编码，我们的方法能够在存在时变混杂因素的情况下捕捉长期依赖关系。值得注意的是，现有模型普遍忽略了可逆表示的重要性，这损害了识别假设的可靠性。为弥补这一缺陷，我们采用信息最大化原则，通过最大化序列数据与其表示之间互信息的下界来优化模型。在合成数据与真实世界数据上的实验表明，我们的方法取得了最先进的反事实估计效果，这标志着对比预测编码在因果推断领域的首次开创性应用。