Prior constraints for causal inference in natural experiments
This talk draws on two projects Sara has been involved in in which constraints were placed on prior distributions in order to obtain causal effect estimates.
The first project is the estimation of the causal effect of statins (a type of cholesterol lowering drug) in the UK population using a regression discontinuity design. We considered both continuous and binary outcomes and imposed constraints on the prior distributions of some parameters in order to stabilise and obtain causal effect estimates.
The second project involved generating continuous values for the severity of non-custodial sentences. A long-standing issue in criminology is that sentence types come in two flavours - custodial sentences measured in days and non-custodial sentences measured as factor levels - making it difficult to compare the two types of outcomes and evaluate the effect of policy changes on sentencing.
We describe a method to extend a continuous severity score based on sentence length to non-custodial outcomes. This method involves using "prior" constraints to impose an ordering by ensuring the severity of non-custodial outcomes cannot exceed certain thresholds. The data thus generated can be used as part of an interrupted time series design to estimate the causal effects of changing sentencing guidelines.
About the speaker
Dr Sara Geneletti’s research interests centre around causal inference. This is the area of statistical methodology concerned with identifying and estimating effects of interventions. She is co-investigator in an MRC project that uses routinely collected medical data in a regression discontinuity design to estimate the effect of drugs in primary care.
Methods for making causal inference often involve adjusting for different types of bias: by selection or due to confounding. These in turn can require the use of data from multiple sources to improve inference. Thus, evidence synthesis is also an area of research that Sara is interested in. Sara is also a Bayesian and all her research is embedded in this paradigm.
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