论文标题
使用条件力矩方法估算异质治疗效果
Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach
论文作者
论文摘要
我们提出了一个新的估计量，用于具有多个外源协变量和潜在的内源治疗变量的部分线性模型（PLM）中的异质治疗效果。我们的方法集成了鲁滨逊转换以处理非参数组件，平滑的最小距离（SMD）方法，以利用条件平均独立性限制以及Neyman正交的一阶条件（POC）。通过采用正规模型选择技术，例如Lasso方法，我们的估计器可容纳众多协变量，同时表现出降低的偏见，一致性和渐近正态性。与传统的GMM型估计器相比，模拟表明其具有不同仪器组的性能。应用这种方法来估算俄勒冈州健康保险实验中医疗补助的异质治疗效果，比传统的GMM方法更可靠，可靠的结果。
We propose a new estimator for heterogeneous treatment effects in a partially linear model (PLM) with multiple exogenous covariates and a potentially endogenous treatment variable. Our approach integrates a Robinson transformation to handle the nonparametric component, the Smooth Minimum Distance (SMD) method to leverage conditional mean independence restrictions, and a Neyman-Orthogonalized first-order condition (FOC). By employing regularized model selection techniques like the Lasso method, our estimator accommodates numerous covariates while exhibiting reduced bias, consistency, and asymptotic normality. Simulations demonstrate its robust performance with diverse instrument sets compared to traditional GMM-type estimators. Applying this method to estimate Medicaid's heterogeneous treatment effects from the Oregon Health Insurance Experiment reveals more robust and reliable results than conventional GMM approaches.
