[论文解读] Estimating Causal Effects Under Interference Using Bayesian Generalized Propensity Scores

Laura Forastiere, Fabrizia Mealli+2 more|arXiv (Cornell University)|Jul 29, 2018

Advanced Causal Inference Techniques被引用 13

一句话总结

本文提出一种贝叶斯广义倾向得分方法，用于在存在干扰的网络化系统中估计直接效应和溢出效应，基于邻域干扰假设和无混淆性假设。该方法结合了惩罚样条回归、社区检测和三步贝叶斯程序，以考虑不确定性和依赖性，在多种网络结构的模拟中表现出稳健性能。

ABSTRACT

In most real-world systems units are interconnected and can be represented as networks consisting of nodes and edges. For instance, in social systems individuals can have social ties, family or financial relationships. In settings where some units are exposed to a treatment and its effect spills over connected units, estimating both the direct effect of the treatment and spillover effects presents several challenges. First, assumptions on the way and the extent to which spillover effects occur along the observed network are required. Second, in observational studies, where the treatment assignment is not under the control of the investigator, confounding and homophily are potential threats to the identification and estimation of causal effects on networks. Here, we make two structural assumptions: i) neighborhood interference, which assumes interference operates only through a function of the immediate neighbors' treatments ii) unconfoundedness of the individual and neighborhood treatment, which rules out the presence of unmeasured confounding variables, including those driving homophily. Under these assumptions we develop a new covariate-adjustment estimator for treatment and spillover effects in observational studies on networks. Estimation is based on a generalized propensity score that balances individual and neighborhood covariates across units under different levels of individual treatment and of exposure to neighbors' treatment. Adjustment for propensity score is performed using a penalized spline regression. Inference capitalizes on a three-step Bayesian procedure which allows to take into account the uncertainty in the propensity score estimation and avoiding model feedback. Finally, correlation of interacting units is taken into account using a community detection algorithm and incorporating random effects in the outcome model.

研究动机与目标

• 解决在单位相互关联且处理效应会扩散到关联单位的观察性研究中估计因果效应的挑战。
• 通过假设个体和邻域处理的无混淆性，克服网络数据中的混淆和同质性偏好。
• 开发一种方法，在不同处理水平间平衡个体和邻域协变量，使用广义倾向得分。
• 通过社区检测和结果模型中的随机效应，对相互作用单位间的依赖性进行建模。
• 提供一个一致的贝叶斯框架，整合倾向得分估计中的不确定性，并避免模型反馈问题。

提出的方法

• 引入邻域干扰假设，即仅个体的直接邻居的处理会影响其结果。
• 定义个体和邻域倾向得分，以在不同处理和暴露水平间平衡协变量。
• 使用惩罚样条回归在结果模型中调整广义倾向得分。
• 实施三步贝叶斯程序，以估计有限样本因果估计算量的后验分布，同时考虑倾向得分估计中的不确定性。
• 通过社区检测引入随机效应，以在结果模型中对相互作用单位间的相关性进行建模。
• 为模型参数指定弱信息先验，并通过后预测检查进行模型验证。

实验结果

研究问题

• RQ1 在存在干扰的观察性网络研究中，如何一致地估计直接效应和溢出效应？
• RQ2 所提出的贝叶斯广义倾向得分方法在多大程度上能减少网络数据中由于混淆和同质性偏好带来的偏差？
• RQ3 该方法在估计不同网络结构（如随机块模型和现实世界社交网络）中的因果效应时表现如何？
• RQ4 倾向得分估计中的不确定性对因果效应估计精度有何影响？
• RQ5 将社区检测与随机效应结合，在捕捉关联单位间依赖性方面有多高效？

主要发现

• 当存在干扰时，与标准方法相比，所提出的方法显著降低了直接效应和溢出效应估计中的偏差。
• 三步贝叶斯程序成功地考虑了倾向得分估计中的不确定性，并避免了模型反馈问题。
• 惩罚样条回归在个体和邻域处理水平上均有效调整了协变量不平衡。
• 社区检测通过捕捉诱导关联单位间依赖性的潜在聚类模式，改善了模型拟合。
• 模拟结果表明，该方法在多种网络类型（包括随机块模型和Add-Health友谊网络）中均表现出稳健性能。
• 后预测检查证实了模型校准良好，且推断过程有效。