基于最优匹配的异质处理效应估计

Optimal matching for heterogeneous treatment effect estimation

在观察性研究中,识别子组和探索异质性具有实际意义。然而,由于缺乏反事实结果和存在选择偏差,基于个体层面的因果推断是一个具有挑战性的问题。为了解决这个问题,我们提出了一个名为TRIMATCH的通用框架,用于估计异质处理效应。首先,通过解决基于三分图网络结构中的最小平均成本流优化问题来找到最佳匹配。其次,利用上一步获得的伪个体处理效应,建立了一个非参数回归模型来预测具有不同特征的个体的异质处理效应。实验结果证明了本文提出的匹配方法的有效性和结果的可解释性。

In observational studies,identifying subgroups and exploring heterogeneity is of practical significance.However,causal inference at the individual level is a challenging problem due to the absence of counterfactual outcomes and the presence of selection bias.To address this issue,we propose a general framework called TRIMATCH for estimating heterogeneous treatment effects.First,we find the optimal matching by solving a minimum average cost flow optimization problem in a tripartite graph network structure.Second,with the pseudo individual treatment effects acquired from the previous step,we establish a nonparametric regression model to predict heterogeneous treatment effects for individuals with diverse characteristics.Our experiments demonstrate the effectiveness of the proposed matching method and the interpretability of the results.