Mathematical Sciences Seminar - Copula Bivariate Binary Models to Control for Residual and Unobserved Confounding
When:
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Venue:
Birkbeck Main Building, Malet Street
No booking required
A method for estimating the effect of a binary treatment on a binary outcome in the presence of unobserved and residual confounding and of non-linear dependence between treatment and outcome is introduced.
Unobserved confounding arises when variables which are associated with both treatment and outcome (hence confounders) are not available. Residual confounding occurs when observed confounders are insufficiently accounted for in the analysis. Treatment and outcome may also exhibit a dependence that cannot be modelled using a linear measure of association. The problem of unobserved confounding is addressed using a two-equation structural latent variable framework, where one equation describes a binary outcome as a function of a binary treatment whereas the other equation determines whether the treatment is received. The residual confounding issue is tackled by modelling flexibly covariate-response relationships using a spline approach. Non-linear dependence between treatment and outcome is dealt with by using copula functions. Related model fitting and inferential procedures are developed and incorporated in the R package SemiParBIVProbit. The method is applied to a case study which uses data from the 2008 Medical Expenditure Panel Survey and whose aim is to estimate the effect of private health insurance on health care utilization.
Contact name:
Department of Economics, Mathematics and Statistics