Multidimensional Structural Regression Model For Causal Inference Under Strongly Ignorable Treatment Assignment

The objective of epidemiology study is to search aetiology, and to measure its causal effect in the light of quantity , thereby prevent from the occurrence of disease . But when a individual has developed the disease in exposed case we cannot measure the probability of who would have developed the disease even if he had not been exposed . Thus we cannot measure exposed effect relative to this individual . In allusion to this problem , epidemiologists and statisticians proposed many solutions . For example , under some assumptions , we approximately replace the exposed effect by estimator of population average causal effect. This dissertation also discuss the problem of this aspect .A multidimensional structural regression model for causal inference is established to estimate the population average treatment effect under strongly ignorable treatment assignment. The maximum likelihood estimator for population average treatment effect is proved to be consistent, unbiased and asymptotically normal .