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Causal inference, probability theory, and graphical insights
Author(s) -
Baker Stuart G.
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5828
Subject(s) - causal inference , inference , graphical model , causal model , econometrics , confounding , plot (graphics) , computer science , statistical inference , contrast (vision) , observational study , causal structure , probability theory , mathematics , statistics , artificial intelligence , physics , quantum mechanics
Causal inference from observational studies is a fundamental topic in biostatistics. The causal graph literature typically views probability theory as insufficient to express causal concepts in observational studies. In contrast, the view here is that probability theory is a desirable and sufficient basis for many topics in causal inference for the following two reasons. First, probability theory is generally more flexible than causal graphs: Besides explaining such causal graph topics as M‐bias (adjusting for a collider) and bias amplification and attenuation (when adjusting for instrumental variable), probability theory is also the foundation of the paired availability design for historical controls, which does not fit into a causal graph framework. Second, probability theory is the basis for insightful graphical displays including the BK‐Plot for understanding Simpson's paradox with a binary confounder, the BK2‐Plot for understanding bias amplification and attenuation in the presence of an unobserved binary confounder, and the PAD‐Plot for understanding the principal stratification component of the paired availability design. Published 2013. This article is a US Government work and is in the public domain in the USA.