论文标题
有条件治疗效果的双重稳定估计：渐近学的研究
Doubly robust estimation for conditional treatment effect: a study on asymptotics
论文作者
论文摘要
在本文中，我们采用双重鲁棒的方法进行估计，当给出了一些协变量时，在参数，半参数和非参数结构下的条件平均治疗效果的滋扰倾向评分和结果回归模型。然后，我们对九个估计量的渐近分布进行了系统研究，这些估计量不同，具有估计倾向得分和结果回归的不同组合。该研究涵盖了正确指定所有模型的渐近性能。倾向得分或结果回归本地 /全球错误指定；并在本地 /全球范围内所有模型中规定。比较了渐近方差，并讨论了模型密码指定下的渐近偏置校正。探索了使用模型 - 密西西比性的渐近方差有时可能比正确指定的所有模型更小的现象。我们还进行了一项数值研究以检查理论结果。
In this paper, we apply doubly robust approach to estimate, when some covariates are given, the conditional average treatment effect under parametric, semiparametric and nonparametric structure of the nuisance propensity score and outcome regression models. We then conduct a systematic study on the asymptotic distributions of nine estimators with different combinations of estimated propensity score and outcome regressions. The study covers the asymptotic properties with all models correctly specified; with either propensity score or outcome regressions locally / globally misspecified; and with all models locally / globally misspecified. The asymptotic variances are compared and the asymptotic bias correction under model-misspecification is discussed. The phenomenon that the asymptotic variance, with model-misspecification, could sometimes be even smaller than that with all models correctly specified is explored. We also conduct a numerical study to examine the theoretical results.
