B0714
Title: A modularized Bayesian factor analysis model for policy evaluation
Authors: Pantelis Samartsidis - University of Cambridge (United Kingdom) [presenting]
Shaun Seaman - University of Cambridge (United Kingdom)
Daniela De Angelis - University of Cambridge (United Kingdom)
Abstract: The problem of estimating the effect of an intervention/policy from time-series observational data on multiple units arises frequently in many fields of applied research, such as epidemiology, econometrics and political science. A Bayesian causal factor analysis model is proposed for estimating intervention effects in such a setting. The model includes a regression component to adjust for observed potential confounders, and its latent component can account for certain forms of unobserved confounding. Further, it can deal with outcomes of mixed type (continuous, binomial, count) and increase efficiency in the estimates of the causal effects by jointly modelling multiple outcomes affected by the intervention. In policy evaluation problems, studying structure in the estimated effects is often of interest. Therefore, the approach to model effect heterogeneity is extended. Specifically, it is demonstrated that modelling effect heterogeneity is not straightforward in causal factor analysis due to non-identifiability. It is then demonstrated how this problem can be circumvented using a modularization approach that prevents post-intervention data from informing a subset of the model parameters. An MCMC algorithm for posterior inference is proposed, and the method is used to evaluate the impact of local tracing partnerships on the effectiveness of England's Test and Trace programme for COVID-19.
