Causal Contrastive Learning for Counterfactual Regression Over Time
This addresses counterfactual regression over time for domains like precision medicine and marketing, but it is incremental as it builds on existing causal inference methods with specific enhancements.
The paper tackles the problem of estimating treatment effects over time for long-term predictions by introducing a method that uses RNNs with Contrastive Predictive Coding and Information Maximization, achieving state-of-the-art results on synthetic and real-world data.
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.