超越线性：多值处理下污染偏差的半参数解决方案

中文摘要 AI 辅助

我们研究了非二元处理下污染偏差的半参数解决方案。深化Goldsmith-Pinkham等人（2024）的讨论，我们详细阐述了样条函数如何在弱函数形式假设下近似条件期望和倾向得分函数。通过重新分析11项研究中的18个回归，我们将标准线性回归与三种污染稳健估计量的参数和半参数版本进行比较。我们记录了参数方法与半参数方法之间较大的点估计差异，并发现采用灵活的半参数解决方案可能不会显著增加统计不确定性。我们建议研究人员验证其结论对使用解决污染偏差的半参数工具的稳健性。

英文摘要

We examine semiparametric solutions to contamination bias for nonbinary treatments. Deepening the discussion by Goldsmith-Pinkham et al. (2024), we detail how spline functions approximate conditional expectation and propensity score functions under weak functional-form assumptions. Reanalyzing 18 regressions across 11 studies, we compare standard linear regressions against parametric and semiparametric versions of three contamination-robust estimators. We document large point-estimate discrepancies between parametric and semiparametric approaches and find that adopting flexible semiparametric solutions may not increase statistical uncertainty substantially. We recommend that researchers verify the robustness of their conclusions to the use of semiparametric tools that address contamination bias.

发表机构

• São Paulo School of Economics - FGV（圣保罗经济管理学院 - 巴西得维加斯基金会）
• Institute of Mathematics and Statistics - USP（数学与统计学院 - 圣保罗大学）

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