Causal inference in high-dimensional generalized linear models with binary outcomes
提出一种针对高维广义线性模型（二元结果）的因果效应去偏估计量，通过单一凸优化平衡协变量并控制方差，无需倾向得分估计，模拟和实际数据表现优于现有方法。
Summary This paper proposes a debiased estimator for causal effects in high-dimensional generalized linear models with binary outcomes and general link functions. The estimator augments a regularized regression plug-in with weights computed from a single convex optimization that approximately balances link-derivative-weighted covariates and controls variance; it does not rely on estimated propensity scores. Under standard conditions, the estimator is $\sqrt{n}$-consistent and asymptotically normal for dense linear contrasts and causal parameters. Simulation results show the superior performance of our approach in comparison to alternatives such as inverse propensity score estimators and double machine learning estimators in finite samples. When applied to National Supported Work training data, our estimates and confidence intervals are close to the experimental benchmark.