95 citations · 95 across the 5 of their papers we have counts for
5 papers · 1 filter
Bayesian Interrupted Time Series for evaluating policy change on mental well-being: an application to England's welfare reform
Connor Gascoigne, Marta Blangiardo, Zejing Shao +4
Factors contributing to social inequalities are also associated with negative mental health outcomes leading to disparities in mental well-being. We propose a Bayesian hierarchical…
Evaluating the Impact of COVID-19 Vaccination in the United Kingdom: A Gaussian Process Approach
Gianluca Giudice, Sara Geneletti, Konstantinos Kalogeropoulos
The rapid rollout of COVID-19 vaccines in the United Kingdom in early 2021 differed markedly from that of many other European countries, providing a natural setting to assess the i…
Bayesian modelling for binary outcomes in the Regression Discontinuity Design
Sara Geneletti, Federico Ricciardi, Aidan O'Keeffe +1
The Regression Discontinuity (RD) design is a quasi-experimental design which emulates a randomised study by exploiting situations where treatment is assigned according to a contin…
Bayesian regression discontinuity designs: Incorporating clinical knowledge in the causal analysis of primary care data
Sara Geneletti, Aidan G. O'Keeffe, Linda D. Sharples +2
The regression discontinuity (RD) design is a quasi-experimental design that estimates the causal effects of a treatment by exploiting naturally occurring treatment rules. It can b…
Direct and Indirect Effects of Sequential Treatments
Vanessa Didelez, Philip Dawid, Sara Geneletti
In this paper we review the notion of direct causal effect as introduced by Pearl (2001). We show how it can be formulated without counterfactuals, using intervention indicators in…