![]() ![]() ![]() To estimate the causal effect of a treatment variable on an outcome variable, special statistical techniques are required for dealing with observational data. This short course introduces propensity score analysis and its applications to causal analysis in observational studies. Because randomized experiments are not always possible in clinical or biomedical studies, researchers often have to meet the challenge of making causal inferences from observational data. For example, the observed relationship between smoking (as a binary treatment variable) and lung cancer (as an outcome variable) is often confounded by the background characteristics of subjects. ![]()
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