A common problem in formulating models for the relative risk and risk difference is the variation dependence between these parameters and the baseline risk, which is a nuisance model. We address this problem by proposing the conditional log odds-product as a preferred nuisance model. This novel nuisance model facilitates maximum-likelihood estimation, but also permits doubly-robust estimation for the parameters of interest.

Linbo Wang is an Assistant Professor in the Department of Statistical Sciences, University of Toronto. His research interests include causal modeling, missing data, graphical models and robust inference in infinite-dimensional models. He is currently interested in discovering the causal structures underlying complex statistical dependencies. His methodological contributions have found useful applications in public health and social sciences.