Bite-sized

Day 1: Thursday, 12 September

The session is structured as a presentation, including a mix of slides, data visualisations, and interactive components with the audience. There will be opportunities for audience questions and discussion. It aims to offer a general introduction of using synthetic control methods in causal inferences and their application in public health research. It will also highlight the importance of considering spatial data structures, patterns, and processes in policy evaluation to ensure accurate and reliable results. Content: 1.) Introduction to synthetic control methods: We begin with an overview of synthetic control methods, explaining how they enable researchers to estimate causal effects in observational studies. 2.) Application in public health policy evaluations: The presenter will discuss specific examples of using synthetic control methods to evaluate the impact of public health policies, such as covid-19 community testing, and vaccination campaigns. 3.) Consideration of spatial data structures: It will delve into the importance of accounting for spatial data structures, such as geographic proximity or spatial autocorrelation, when conducting policy evaluations in public health. 4.) Analysis of spatial patterns and processes: Attendees will learn about techniques for analysing spatial patterns and processes in health outcomes data, and how these analyses can inform policy decisions. 5.) Practical examples: The session will include practical examples to illustrate the concepts discussed and demonstrate how synthetic control methods can be applied in real-world scenarios. 6.) Discussion, Q&A: The session will conclude with a discussion where attendees can ask questions, share insights, and engage in dialogue with the presenter(s) and fellow participants.