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MARMoT: a novel approach to handle propensity score techniques with many treatments
##manager.scheduler.building##: Velodromo - Bocconi University
##manager.scheduler.room##: N03
Date: 2019-01-26 11:00 AM – 12:30 PM
Last modified: 2018-12-26
Abstract
Neighbourhood effects have been defined by Oakes (2004) as the independent causal effects of neighbourhood on a given number of health or social outcome(s).The aim of our work is to estimate the neighbourhood effect on old population in Turin with a propensity score approach.To achieve this goal, we need to work on adapting propensity score techniques to work well in a framework with many treatments (with ten or more treatments) and we used data from the Turin Longitudinal Study (SLT).Our main goal is to understand if the observed differences in health outcomes across neighbourhoods can be causally attributed to neighbourhoods' as opposed to their different composition, i.e. to the fact that individuals with different risks factors live in different areas.In order to adjust for confounders and simulate an experimental approach, we focused on propensity score techniques and we proposed a novel method that consists on a matching based on partially ordered sets (poset). The Matching on Posed based Average Rank for Multiple Treatment (MARMoT), tested with some simulations, has revealed to be really useful to estimate neighbourhood effect, reducing bias of estimates because of the initial improvement of covariates' balance between groups.
Keywords
Matching; Multi-treatment; Poset; Neighbourhood effect