Propensity score regression for causal inference with treatment heterogeneity
用于异质性治疗效应因果推断的倾向得分回归AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究倾向得分、异质性治疗效应及其非参数估计，属于因果推断；未明确涉及分位数或分布效应。
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用于异质性治疗效应因果推断的倾向得分回归
- 研究问题
- 当关注的个体特征只是完整协变量的一部分，且倾向得分接近零或一时，如何较可行地非参数估计随这些特征变化的治疗效应。
- 方法与思路
- 作者提出两步倾向得分回归：先对倾向得分及关注特征进行非参数回归获得中间估计，再将中间估计对关注特征回归。
- 材料陈述的贡献
- 摘要称，方法缓解计算困难并具有一致性、渐近正态性和显式方差估计；模拟展示极端倾向得分情景表现，并应用于流感调查。
- 阅读关注点
- 应核对因果识别与重叠条件、两步回归的平滑和误差传播要求，以及局部不敏感性的含义、方差估计和极端得分比较设计。
- 阅读边界
- 这是arXiv预印本，同行评审或期刊接收未核实；摘要未列出理论条件，对极端倾向得分较稳定不等于消除了无重叠时的识别问题。
原文摘要
Understanding how treatment effects vary on individual characteristics is critical in the contexts of personalized medicine, personalized advertising and policy design. When the characteristics are of practical interest are only a subset of full covariate, non-parametric estimation is often desirable; but few methods are available due to the computational difficult. Existing non-parametric methods such as the inverse probability weighting methods have limitations that hinder their use in many practical settings where the values of propensity scores are close to 0 or 1. We propose the propensity score regression (PSR) that allows the non-parametric estimation of the heterogeneous treatment effects in a wide context. PSR includes two non-parametric regressions in turn, where it first regresses on the propensity scores together with the characteristics of interest, to obtain an intermediate estimate; and then, regress the intermediate estimates on the characteristics of interest only. By including propensity scores as regressors in the non-parametric manner, PSR is capable of substantially easing the computational difficulty while remain (locally) insensitive to any value of propensity scores. We present several appealing properties of PSR, including the consistency and asymptotical normality, and in particular the existence of an explicit variance estimator, from which the analytical behaviour of PSR and its precision can be assessed. Simulation studies indicate that PSR outperform existing methods in varying settings with extreme values of propensity scores. We apply our method to the national 2009 flu survey (NHFS) data to investigate the effects of seasonal influenza vaccination and having paid sick leave across different age groups.