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Jennifer Starling Gives Invited Talk at the 2019 Joint Statistical Meetings

Jennifer Starling Gives Invited Talk at the 2019 Joint Statistical Meetings

15 August 2019— Doctoral student Jennifer Starling gave an invited talk on her research, titled "Targeted Smooth Bayesian Causal Forests for Heterogeneous Smooth Treatment Effects," at the 2019 Joint Statistical Meetings held in Denver, Colorado during July 27 – August 1, 2019.

The work presented extends the Bayesian Causal Forest framework to include cases where researchers want to induce smoothness over a single covariate in the observational data framework. The goal of the project is to give the leading abortion provider in the UK, British Pregnancy Advisory Service, better tools for giving personalized advice to their patients. The research project examines the relative effectiveness of administering two early medical abortion medications, mifeprisone and misoprostol, simultaneously versus with a 24-48 hour interval in between, and how the relative effectiveness changes over gestation.

Jennifer is beginning her fourth year in the the PhD in Statistics program in Fall 2019, advised by Dr. James Scott. Her research focuses on novel Bayesian methods and causal inference work with biomedical and public health applications. The above work is in collaboration with subject-matter experts Dr. Patricia Lohr, the medical director of British Pregnancy Advisory Service, and Dr. Abigail R.A. Aiken, of the LBJ school. Statistics collaborators are Drs. Jared Murray, Carlos Carvalho, and James Scott.

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