Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators
通过矩阵加权线性估计量融合观测与干预数据估计因果效应
We study causal effect estimation from a mixture of observational and interventional data in a confounded linear regression model with multivariate treatments. We show that the statistical efficiency in terms of expected squared error can be improved by combining estimators arising from both the observational and interventional setting. To this end, we derive methods based on matrix weighted linear estimators and prove that our methods are asymptotically unbiased in the…