Contact Information
Department of Economics
Northwestern University
2211 Campus Drive, 3rd Floor
Evanston, IL 60208
Phone: 507.254.9276
Curriculum Vitae
Education
PhD, Economics, Northwestern University, 2020 (expected)
MA, Economics, Northwestern University, 2015
BS, Economics and Mathematics, University of Minnesota, 2014
Field of Specialization
Econometrics
Job Market Paper
“Identification and Estimation of Treatment Effects with Instrumental Variables under Data Combination”
[View PDF] [View in Dropbox]
Abstract: I characterize sharp bounds on treatment effects under data combination with instrumental variables. Data combination in this paper refers to having multiple samples drawn from the same population in which observations cannot be linked across samples. I allow for subsets of the outcome, treatment, instrument and covariates to be observed across these samples. The parameters I can bound include the average treatment effect and certain policy relevant treatment effects. The sharp identified upper and lower bounds for the parameter of interest can each be expressed as the optimal value of the objective function in a linear programming problem where the coefficients are probabilities identified from the samples, under certain conditions. These conditions include standard instrumental variables assumptions allowing for heterogeneous effects, finite range of random variables, and a condition regulating which combinations of variables can be observed across samples. This identification strategy forms the basis for estimation, although estimation is not as simple as replacing the identified coefficients with sample estimates. The application to algorithmic bail reform in Philadelphia suggests that, if a freely available algorithm were used to determine pretrial release, the incarceration rate would decrease under the commonly used monotonicity assumption. The results of this application are dependent on the choice of shape restrictions one is willing to make.
Working Paper
“Tuning Parameter Selection in the Synthetic Control Method”
[View PDF]
Abstract: In this paper I show an asymptotically optimal choice of a weighting matrix used in the synthetic control method. The synthetic control method takes a weighted average of outcomes for untreated units to estimate the outcome under no treatment for a treated unit. This can then be used to estimate a treatment effect for the treated unit. The weights are chosen such that the weighted average of the outcomes in the pretreatment time periods and of covariates approximates that of the treated unit. In practice, these weights are chosen to minimize a distance which depends on a weighting matrix. I show asymptotic optimality of a leave-one-out cross-validation procedure to choose this weighting matrix. This amounts to performing the synthetic control method, in turn, as if each of the untreated units were instead treated and assessing the prediction on the untreated units for a given weighting matrix. This is not straightforward because there is dependence across these synthetic control estimates.
References
Prof. Ivan Canay (Committee Chair)
Prof. Joel Horowitz
Prof. Eric Auerbach