title: Causal inference, probability theory, and graphical insights.
abstract: 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.
authors: Biometry Research Group, National Cancer Institute, Bethesda, MD 20892, USA. sb16i@nih.gov
authors: Stuart G
authors: SG
authors: Baker
mesh_terms: D015982:Bias; D056808:Biostatistics / Q000379:methods*; D015984:Causality; D006801:Humans; D015233:Models, Statistical; D019370:Observation; D011338:Probability Theory; D012107:Research Design
keywords: BK-Plot; Simpson's paradox; causal graph; confounder; instrumental variable; observational study
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