Self-supervised Causal Effects Estimation
TL;DR
Self-supervised Causal Effects Estimation is proposed, a novel framework that integrates causal priors with self-supervised learning to construct balanced and predictive representations for causal effects estimation that consistently outperforms state-of-the-art methods.
Abstract
Causal effect estimation is fundamental to personalized decision-making and policy evaluation, with applications spanning healthcare, economics, and social sciences. However, observational data often suffer from selection bias and the absence of counterfactual outcomes, posing significant challenges to inference accuracy. While recent representation learning-based approaches have shown promise, they fail to fully exploit the rich self-supervised information and causal prior knowledge embedded in the data. To address these limitations, we propose Self-supervised Causal Effects Estimation (SCEE), a novel framework that integrates causal priors with self-supervised learning to construct balanced and predictive representations for causal effects estimation. Experimental results on widely used real-world, semi-synthetic, and synthetic benchmarks demonstrate that SCEE consistently outperforms state-of-the-art methods. To further enhance its effectiveness, we investigate different contrastive sample selection strategies, maximizing the potential of contrastive learning in causal inference. Additionally, we analyze the impact of sample reweighting and show that SCEE inherently mitigates distributional discrepancies between treatment and control groups, eliminating the need for explicit reweighting mechanisms.