网络干扰下因果推断中模型设定的随机化检验
Randomization tests for model specification in causal inference under network interference
AI总结：
针对网络干扰下因果推断中暴露映射模型设定偏差问题，提出基于设计的模型设定框架及随机化检验程序，经理论验证与模拟、实地实验证实其有效性与良好功效。
AI中文摘要：
当研究总体由网络连接时，实验数据分析会变得具有挑战性。暴露映射是文献中用于定义和估计溢出效应的常用工具，它能降低估计量的维度，从而促进可识别性。现有研究通常假设该映射设定正确，将暴露映射的选择留给分析人员，这使得溢出效应的估计量（如Horvitz-Thompson估计量）易受模型设定偏差的影响。尽管这些估计量已被证明对某些形式的受控设定偏差具有稳健性，但在实证研究合适的暴露映射方面，方法学进展相对较少。本文提出一种用于因果推断的新型基于设计的模型设定框架，在此基础上开发了一种随机化检验程序，以评估存在网络干扰时暴露映射模型的正确设定。我们为所提检验程序的渐近有效性提供了理论保证，通过广泛的模拟研究确立了该方法的优异功效特性，并在一项调查青少年反冲突规范影响的实地实验中对其进行了验证。
英文摘要：
Analysis of experimental data becomes challenging when the underlying population is connected by a network. Exposure mapping is a common tool in the literature for defining and estimating spillover effects. These mappings reduce the dimensionality of the estimand, thereby facilitating identifiability. It is assumed that this mapping is correctly specified, leaving the choice of the exposure mapping to the analyst. This makes estimators of the spillover effect, such as the Horvitz-Thompson estimator, vulnerable to bias from model misspecification. Although these estimators have been shown to be robust to certain forms of controlled misspecification, there has been relatively little methodological progress in empirically investigating appropriate exposure mappings. In this paper, we propose a novel design-based model specification framework for causal inference. Building on this, we develop a randomization-testing procedure to assess the correct specification of an exposure-mapping model in the presence of network interference. We provide theoretical guarantees for the asymptotic validity of the proposed testing procedure. We establish the favorable power properties of our method through an extensive simulation study and illustrate it in a field experiment investigating the effect of anti-conflict norms among adolescents.
