Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model for estimating intervention effects in such settings and develop an efficient Markov chain Monte Carlo algorithm to sample from the high-dimensional and non-tractable posterior of interest. The proposed method is one of the few that can simultaneously deal with outcomes of mixed type (continuous, binomial, count), increase efficiency in the estimates of the causal effects by jointly modelling multiple outcomes affected by the intervention (as shown via a simulation study), and easily provide uncertainty quantification for all causal estimands of interest. Using the proposed approach, we evaluate the impact that Local Tracing Partnerships had on the effectiveness of England’s Test and Trace programme for COVID-19.

Value Added Tax (VAT) fraud erodes public revenue and puts legitimate businesses at a disadvantaged position thereby impacting inequality. Identifying and combating VAT fraud before it occurs is therefore important for welfare. This paper proposes flexible machine learning algorithms which detect fraudulent transactions by utilising the information provided by the VAT network structure. It does so by recognising that VAT fraud is a group or community activity which has to be detected by relying on heterogeneous data of huge dimension. VAT fraud detection is implemented through a combination of a suitably constructed Laplacian matrix with classification algorithms that rely on scalable machine learning techniques.