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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]

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]

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]

By Thomas S. Richardson, Robin J. Evans, James M. Robins, Ilya Shpitser.

Byline verified in the primary arXiv record. Introduction and section on latent projections: directed mixed graphs for margins of hidden-variable directed acyclic graphs.