Randomization, statistics, and causal inference.
This paper reviews the role of statistics in causal inference. Special attention is given to the need for randomization to justify causal inferences from conventional statistics, and the need for random sampling to justify descriptive inferences. In most epidemiologic studies, randomization and random sampling play little or no role in the assembly of study cohorts. I therefore conclude that probabilistic interpretations of conventional statistics are rarely justified, and that such interpretations may encourage misinterpretation of nonrandomized studies. Possible remedies for this problem include deemphasizing inferential statistics in favor of data descriptors, and adopting statistical techniques based on more realistic probability models than those in common use.
Greenland
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Department of Epidemiology, UCLA School of Public Health 90024-1772.
D001499:Bayes Theorem; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*