Computational Approaches to Identification and Inference for Causal Parameters in Experiments and Quasi-Experiments
Developing new computational approaches to identification and inference for causal parameters in experimental and quasi-experimental settings (factorial experiments with noncompliance, augmented designs, and judge/examiner instruments). The project applies these methods to labor, development, and medical economics, focusing on computationally feasible methods for partially identified parameters.
There are two distinct RA positions, one for helping primarily with coding and the more formal aspects of the work, and the other helping primarily with literature review of the relevant empirical literature and working with data for applied examples.
Requisite Skills and Qualifications
For either position: Econ 136 or equivalent; LaTeX. Coding position: Linear algebra, multivariate calculus, linear programming; MATLAB and/or R. Literature/Empirical position: R; interest in policy evaluation (labor, development, education, medicine, or political science).
For position focused on literature review and empirical work: (a) be comfortable with coding skills in R; (b) have interests in policy evaluation in at least one of the following fields: labor economics, development economics, education economics, medicine, or the get out the vote literature in political science.