Causal inference for complex longitudinal data: The continuous case
成果类型：
Article
署名作者：
Gill, RD; Robins, JM
署名单位：
Utrecht University; Harvard University; Harvard T.H. Chan School of Public Health
刊物名称：
ANNALS OF STATISTICS
ISSN/ISSBN：
0090-5364
发表日期：
2001
页码：
1785-1811
关键词：
摘要：
We extend Robins' theory of causal inference for complex longitudinal data to the case of continuously varying as opposed to discrete covariates and treatments. In particular we establish versions of the key results of the discrete theory: the g-computation formula and a collection of powerful characterizations of the g-null hypothesis of no treatment effect. This is accomplished under natural continuity hypotheses concerning the conditional distributions of the outcome variable and of the covariates given the past, We also show that our assumptions concerning counterfactual variables place no restriction on the joint distribution of the observed variables: thus in a precise sense, these assumptions are for free, or if you prefer, harmless.