Causal Contrastive Learning for Counterfactual Regression Over Time
<|reference_start|>Causal Contrastive Learning for Counterfactual Regression Over Time: 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.<|reference_end|>
@article{bouchattaoui2024causal,
    title={Causal Contrastive Learning for Counterfactual Regression Over Time},
    author={Mouad El Bouchattaoui, Myriam Tami, Benoit Lepetit, Paul-Henry
  Courn`ede},
    journal={arXiv preprint arXiv:2406.00535},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00535},
    primaryClass={cs.LG stat.ME}
}