论文标题
基于基于IPW的有条件平均治疗效果的估计
On IPW-based estimation of conditional average treatment effect
论文作者
论文摘要
本文中的研究对有条件的平均治疗效应的四个反相反概率加权（IPW）的估计量的渐近行为进行了系统的研究，分别是非参数，半磁化，参数估计和真实的倾向分数。为此，我们首先要特别关注半参数降低降低结构，以便我们可以很好地研究基于半摩擦的估计器，该估计器可以很好地减轻维度的诅咒，并大大避免模型错误指定。我们还获得了具有非参数估计倾向分数的现有估计量的进一步属性。根据它们的渐近方差函数，研究揭示了其渐近效率的一般排名。在哪种情况下，渐近等效性可以保持；给定协变量在倾向得分，带宽和内核选择的一组中的隶属关系的关键作用。结果显示与基于IPW的（无条件）平均治疗效果（ATE）有根本差异。数值研究表明，对于高维范例，基于半摩托的估计器通常表现良好（而基于非参数的估计器，甚至有时甚至是基于参数的估计器，也会受到维度的影响更大。进行了一些数值研究以检查其表现。分析一个真实的数据示例以进行插图。
The research in this paper gives a systematic investigation on the asymptotic behaviours of four inverse probability weighting (IPW)-based estimators for conditional average treatment effect, with nonparametrically, semiparametrically, parametrically estimated and true propensity score, respectively. To this end, we first pay a particular attention to semiparametric dimension reduction structure such that we can well study the semiparametric-based estimator that can well alleviate the curse of dimensionality and greatly avoid model misspecification. We also derive some further properties of existing estimator with nonparametrically estimated propensity score. According to their asymptotic variance functions, the studies reveal the general ranking of their asymptotic efficiencies; in which scenarios the asymptotic equivalence can hold; the critical roles of the affiliation of the given covariates in the set of arguments of propensity score, the bandwidth and kernel selections. The results show an essential difference from the IPW-based (unconditional) average treatment effect(ATE). The numerical studies indicate that for high-dimensional paradigms, the semiparametric-based estimator performs well in general {whereas nonparametric-based estimator, even sometimes, parametric-based estimator, is more affected by dimensionality. Some numerical studies are carried out to examine their performances. A real data example is analysed for illustration.
