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Statistics textbooks provide interesting examples of causal questions: Did halothane do more to cause surgical deaths than ether? Was the lower admission rate of women to graduate programs at the University of California caused by discrimination against women? Does smoking cause cancer? Issues about determining causes surround many of the introductory and even advanced topics in statistical pedagogy: experimental design, randomization, collinearity in multiple regression, observational versus experimental studies, and so forth. But except for the standard warnings that correlation is not causation, the textbooks include little if any systematic discussion of the connection between causation and probability. The mathematics of probability and statistical inference is explicit, but the connection between probability relations and causal dependencies is almost completely tacit. The same applies to prediction, at least outside of econometrics. The textbooks consider cases where policy interventions are at issue, but they tell us nothing systematic about the connections between statistical analysis of observations or experiments and predictions of the effects of policies, actions or manipulations.
KeywordsDirected Graph Causal Inference Causal Structure Conditional Independence Markov Condition
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