A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes.

ABSTRACT: A problem that is frequently encountered in many areas of scientific research is that of estimating the effect of a non-randomized binary intervention on an outcome of interest by using time series data on units that received the intervention ('treated') and units that did not ('controls'). One popular estimation method in this setting is based on the factor analysis (FA) model. The FA model is fitted to the preintervention outcome data on treated units and all the outcome data on control units, and the counterfactual treatment-free post-intervention outcomes of the former are predicted from the fitted model. Intervention effects are estimated as the observed outcomes minus these predicted counterfactual outcomes. We propose a model that extends the FA model for estimating intervention eff

SUBMITTER: Samartsidis P 

PROVIDER: S-EPMC7612111 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature