Abstract
The problem of estimating the effect of an intervention/policy from time-series observational data on multiple units arises frequently in many fields applied research such as epidemiology, econometrics and political science. In this talk, we propose a Bayesian causal factor analysis model 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, it is often of interest to study structure in the estimated effects. We therefore extend our approach to model effect heterogeneity. Specifically, we demonstrate that modelling effect heterogeneity is not straightforward in causal factor analysis, due to non-identifiability. We then demonstrate 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.
Organizzazione
Simone Tiberi