@MISC{Spirtes_thelimits, author = {Peter Spirtes}, title = {The Limits of Causal Inference from Observational Data}, year = {} }
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Abstract
Introduction The following quotation from Rosenbaum (1995) expresses a commonly held view about the problem of potential confounders, and how they can be dealt with. (We will take a "confounder" of treatment and response to be a variable that is a cause of both treatment and response.) An observational study is an empirical investigation of treatments, policies, or exposures and the effect they cause, but it differs from an experiment in that the investigator cannot control the assignment of treatments to subjects. ... Analytical adjustments are widely used in observational studies to remove overt biases, that is, differences between treated and control groups, present before treatment, that are visible in the data at hand. ... If treated and control groups differed before treatment in ways not recorded, there would be a hidden bias. ... sensitivity analyses ... ask how the findings of a study might be altered by hidden biases of various magnitudes. It turns out that