Multidimensional Structural Regression Model for Causal Inference under Strongly Ignorable Treatment Assignment

Abstract

In the study of epidemiology aetiology, we usually cannot measure exposed effect relative to an individual, but under some assumptions, we approximately replace the exposed effect by estimator of population average causal effect. A multidimensional structural regression model for causal inference is established to estimate the population average treatment effect under strongly ignorable treatment assignment. Under the normal distribution, the maximum likelihood estimator for population average treatment effect is proved to be consistent, unbiased and asymptotically normal.