= Miguel A. Hernán
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<Miguel A. Hernán> develops <causal inference> methods for epidemiological data. With <James M. Robins>, he explains how <observational studies> can estimate treatment effects when the treatment groups satisfy appropriate <conditional exchangeability> assumptions.<Estimating causal effects from epidemiological data by Miguel A. Hernán and James M. Robins>[Original byline and abstract]{ref}
One method is <outcome standardization>: estimate outcome risks within baseline <covariate> strata under each treatment and average both sets of risks over the same target population. The comparison depends on identifying assumptions, including adequate measurement of <confounders>, <consistency of potential outcomes> and <positivity in causal inference>.<Estimating causal effects from epidemiological data by Miguel A. Hernán and James M. Robins>[Standardisation discussion and identification conditions]{ref}
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