Bayesian causal inference for zero-inflated GLMs using a potential outcomes framework

Speaker: Ben Swallow, University of St Andrews

Abstract

We propose a method for conducting Bayesian causal inference under a generalised linear model potential outcomes framework, for data where there are many more zeros than would naturally be expected. We develop an approach using both semi-continuous and fully continuous probability distributions and apply the approach to both simulated data and ornithological citizen science data in the UK, comparing the results to purely observational studies. Further analyses of the contrasting GLMs are also discussed.