Causal Contrastive Learning for Counterfactual Regression Over Time

Mouad El Bouchattaoui, Myriam Tami, BENOIT LEPETIT, Paul-Henry Cournède

Keywords: Counterfactual Regression, Longitudinal Data, Contrastive Learning

TL;DR: We study the problem of counterfactual regression over large time horizons leveraging contrastive learning.

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 within 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.

Primary Area: Causal inference

Submission Number: 20728