Causal Contrastive Learning for Counterfactual Regression Over Time

• URL: http://arxiv.org/abs/2406.00535v3
• Date: Tue, 29 Oct 2024 00:12:20 GMT
• Title: Causal Contrastive Learning for Counterfactual Regression Over Time
• Authors: Mouad El Bouchattaoui, Myriam Tami, Benoit Lepetit, Paul-Henry Cournède,
• Abstract summary: 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. Our method achieves state-of-the-art counterfactual estimation results using both synthetic and real-world data.
• Score: 3.3523758554338734
• License:
• Abstract: 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.

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