Double Machine Learning based Program Evaluation under Unconfoundedness

Assessing Sensitivity to Unconfoundedness: Estimation and Inference

This paper provides a set of methods for quantifying the robustness of treatment effects estimated using the unconfoundedness assumption (also known as selection on observables or conditional independence). Specifically, we estimate and do inference on bounds on various treatment effect parameters, like the average treatment effect (ATE) and the average effect of treatment on the treated (ATT), under nonparametric relaxations of the unconfoundedness assumption indexed by a scalar sensitivity parameter c. These relaxations allow for limited selection on unobservables, depending on the value of …

连续处理下的双重去偏机器学习非参数推断

Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments

本文提出连续处理因果效应的双重稳健 DML 推断框架，结合核矩函数与交叉拟合，使非参数或高维 nuisance 估计不影响一阶渐近分布，并通过模拟和职业培训评估验证。

具有弱重叠的双重稳健估计器

Doubly Robust Estimators with Weak Overlap

本文提出一种在修剪倾向分数后仍保持双重稳健性的估计方法，通过偏倚校正提升估计精度，在四个应用中展示了更精确的结果。

Program Evaluation with Right-Censored Data

In a unified framework, we provide estimators and confidence bands for a variety of treatment effects when the outcome of interest, typically a duration, is subjected to right censoring. Our methodology accommodates average, distributional, and quantile treatment effects under different identifying assumptions including unconfoundedness, local treatment effects, and nonlinear differences-in-differences. The proposed estimators are easy to implement, have close-form representation, are fully data-driven upon estimation of nuisance parameters, and do not rely on parametric distributional assumpt…

平均处理效应的双重稳健贝叶斯推断

Double Robust Bayesian Inference on Average Treatment Effects

提出一种双重稳健贝叶斯推断方法估计平均处理效应，通过调整先验和后验并建立半参数Bernstein-von Mises定理，实现渐近精确覆盖的置信区间，模拟和实证中表现优于现有方法。

内生选择与溢出：政策相关因果效应的贝叶斯推断

Endogenous Selection and Spillovers: Bayesian Inference for Policy-Relevant Causal Effects

本文提出含溢出的罗伊模型，开发贝叶斯推断算法，将其应用于美国机会区计划，发现该计划对住房发展有正向直接效应但溢出收益有限，扩张回报递减。