Causal aggregation: estimation and inference of causal effects by constraint-based data fusion
因果聚合:基于约束的数据融合与联合干预效应推断
In causal inference, it is common to estimate the causal effect of a single treatment variable on an outcome. However, practitioners may also be interested in the effect of simultaneous interventions on multiple covariates of a fixed target variable. We propose a novel method that allows to estimate the effect of joint interventions using data from different experiments in which only very few variables are manipulated. If there is only little randomized data or no randomized…