Graphical identifiability criteria for causal effects in studies with an unobserved treatment/response variable

• 成果类型: Article
• 署名作者: Kuroki, Manabu
• 署名单位: University of Osaka
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asm005
• 发表日期: 2007
• 页码: 3747
• 关键词: single-factor model; identification; bounds

摘要

We consider the problem of using data in studies with an unobserved treatment/response variable in order to evaluate average causal effects, when cause-effect relationships between variables can be described by a directed acyclic graph and the corresponding recursive factorization of a joint distribution. The paper proposes graphical criteria to test whether average causal effects are identifiable even if a treatment/response variable is unobserved. If the answer is affirmative, we provide further formulations for average causal effects from the observed data. The graphical criteria enable us to evaluate average causal effects when it is difficult to observe a treatment/response variable.