Laura Balzer, Assistant Professor of Biostatistics at the University of Massachusetts Amherst, will teach a full-day short course titled "Introductory and advanced methods for causal inference" as part of the 32nd New England Statistics Symposium being held in the UMass Amherst Campus Center.
A description of the one-day course, which runs from 8:00 am to 5:00 pm on Friday, April 13, can be found below. For more details, or to register, visit the event website at https://symposium.nestat.org/. Event registration closes on April 6th.
With the recent and ongoing ‘data explosion’, methods to delineate causation from correlation are perhaps more pressing now than ever. This course will introduce a general framework for causal inference: 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 methods, and 5) interpretation of findings. The focus is on effect estimation for exposures occurring at a single time point, and extensions for longitudinal effects are also presented. The estimation methods include G-computation, inverse probability of treatment weighting (IPTW), and targeted maximum likelihood estimation (TMLE) with Super Learner. Participants gain practical experience with an applied example and implement these estimators in R.