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.[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.[ref]
Estimating causal effects from epidemiological data by Miguel A. Hernán and James M. Robins. Standardisation discussion and identification conditions
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