Causal Contrastive Learning for Counterfactual Regression Over Time

TL;DR

This paper presents a novel RNN-based approach with contrastive predictive coding and InfoMax principles for long-term counterfactual regression, outperforming transformer-based models in efficiency and accuracy.

Contribution

It introduces the first use of Contrastive Predictive Encoding in causal inference, enhancing long-term treatment effect estimation with invertible representations.

Findings

Achieves state-of-the-art counterfactual estimation results

Effectively captures long-term dependencies with CPC and RNNs

Avoids computational costs of transformer models

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.