= James M. Robins
{c}
<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.<Estimating causal effects from epidemiological data by Miguel A. Hernán and James M. Robins>[Original byline, abstract and weighting discussion]{ref}
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.<Estimating causal effects from epidemiological data by Miguel A. Hernán and James M. Robins>[Inverse-probability weighting and its causal conditions]{ref}
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