Randomized controlled trials (RCTs) represent the gold standard for evaluating the effect of treatments within both medicine and the social sciences. However, if the effect of the treatment varies across subjects, and enrollment into the RCT is related to individual-level treatment effect heterogeneity, then the estimated effect of the treatment from the RCT may not be indicative of the effect of the treatment in the population of interest. This project will develop the econometric methodology for combining data from an RCT with nonexperimental data to adjust for nonrandom selection of participants into the RCT with the goal of providing results with greater external validity than would be possible from the RCT alone.
Requisite Skills and Qualifications:
There are two separate RA positions: (1) someone to investigate relevant literature and especially relevant data, including data registries in both economics and medicine, and (2) someone to help write the code to implement the methodology, should be comfortable with programming and be comfortable with (or willing to learn) linear programming and generalizations of linear programming.