James M. Robins develops statistical methods for causal inference in epidemiology. His joint exposition with Miguel A. Hernán explains how inverse probability weighting can estimate population treatment effects from observational studies under appropriate identifying assumptions.[ref]
Estimating causal effects from epidemiological data by Miguel A. Hernán and James M. Robins. Original byline, abstract and weighting discussion
Weighting gives more influence to people whose observed treatment was relatively unlikely given their measured baseline covariates. In the resulting weighted comparison, measured treatment-selection differences are removed under the assumed model. Validity still depends on conditional exchangeability, consistency of potential outcomes and positivity in causal inference; extreme weights also expose a practical difficulty when some treatment choices are rare.[ref]
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