SELECT * FROM sessions where years='2017' and sequence=121
December 4-8, 2017 at Atlantic City, New Jersey
The 73rd Deming Conference on Applied Statistics
Sponsored by Metropolitan Section, ASQ and Biopharmaceutical Section, ASA
   
abstract
      

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Causal Inference in a Big Data World: Introduction to Parametric and Semi-Parametric Estimators for Causal Inference

Speaker(s): Dr. Laura Balzer, Assistant Professor of Biostatistics at the University of Massachusetts, Amherst

Moderator: Alfred H. Balch
 

At increasing velocity, volume and variety, we are generating, recording and storing unprecedented amounts of data. Along with its many challenges, Big Data present exciting opportunities to better understand risk factors, to build improved predictors, and to examine the causal relationships between variables. Still, there are many sources of association between two variables, including direct effects, indirect effects, measured confounding, unmeasured confounding, and selection bias. Methods to delineate causation from correlation are perhaps more pressing now than ever. This short course will introduce a “causal roadmap” to approach research questions: 1) clear statement of the scientific question, 2) definition of the causal model and parameter of interest, 3) assessment of identifiability – that is, linking the causal effect to a parameter estimable from the observed data distribution, 4) choice and implementation of estimators including parametric and semi-parametric, and 5) interpretation of findings. The focus will be on estimation with parametric G-computation, inverse probability of treatment weighting (IPTW), and targeted maximum likelihood estimation (TMLE) with SuperLearner. Participants will work through the roadmap using an applied example and implement these estimators in R during the workshop session.



Dr. Laura Balzer is an Assistant Professor of Biostatistics at the University of Massachusetts, Amherst. She earned her PhD in Biostatistics from the University of California, Berkeley and completed her post-doctoral studies at the Harvard T.H. Chan School of Public Health. Laura is a methodologist with substantive interests in global health, community-based participatory research, and social determinants of health. Her particular areas of expertise are Causal Inference and Machine Learning. She is the Principal Statistician for the SEARCH trial, a 320,000-person community randomized trial to evaluate a bold strategy for HIV testing and treatment in rural Uganda and Kenya (NCT01864603). Laura is also passionate about teaching introductory and advanced causal and statistical methods. Jointly with Dr. Maya Petersen, she was awarded the 2014 ASA’s Causality in Statistics Education Award.

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