🤖 AI Summary
In causal effect estimation, historical decision policies induce distributional shifts between treatment and control groups, causing inverse probability weighting (IPW) to suffer from instability due to propensity score estimation bias and extreme weights. This paper proposes a distributionally robust causal estimation framework that—uniquely—integrates distributionally robust optimization (DRO) with weighted Rademacher complexity regularization: DRO mitigates ambiguity in propensity score estimation, while the regularization curbs statistical variance of weighted estimators. We approximate the DRO objective via a computationally tractable adversarial loss, preserving theoretical rigor while enhancing practicality. Extensive experiments on multiple synthetic and real-world datasets demonstrate that our method consistently outperforms state-of-the-art IPW and DRO baselines across estimation accuracy, robustness to distributional shift, and estimator stability.
📝 Abstract
Causal inference requires evaluating models on balanced distributions between treatment and control groups, while training data often exhibits imbalance due to historical decision-making policies. Most conventional statistical methods address this distribution shift through inverse probability weighting (IPW), which requires estimating propensity scores as an intermediate step. These methods face two key challenges: inaccurate propensity estimation and instability from extreme weights. We decompose the generalization error to isolate these issues--propensity ambiguity and statistical instability--and address them through an adversarial loss function. Our approach combines distributionally robust optimization for handling propensity uncertainty with weight regularization based on weighted Rademacher complexity. Experiments on synthetic and real-world datasets demonstrate consistent improvements over existing methods.