Dmitry Arkhangelsky

PhD Program, Economics
PhD Program Office
Graduate School of Business
Stanford University
655 Knight Way
Stanford, CA 94305

Dmitry Arkhangelsky

I am a Ph.D. candidate in Economics on the job market during the 2017-2018 academic year. I will be available for interviews at the 2018 ASSA Annual Meeting in Philadelphia.
Research Interests
Econometrics
Causal Inference

Job Market Paper

Dealing with a Technological Bias: The Difference-in-Difference Approach
I construct a nonlinear model for causal inference in the empirical settings where researchers observe individual-level data for few large clusters over at least two time periods. It allows for identification (sometimes partial) of the counterfactual distribution, in particular, identifying average treatment effects and quantile treatment effects. The model is flexible enough to handle multiple outcome variables, multidimensional heterogeneity, and multiple clusters. It applies to the settings where the new policy is introduced in some of the clusters, and a researcher additionally has information about the pretreatment periods. I argue that in such environments we need to deal with two different sources of bias: selection and technological. In my model, I employ standard methods of causal inference to address the selection problem and use pretreatment information to eliminate the technological bias. In case of one-dimensional heterogeneity, identification is achieved under natural monotonicity assumptions. The situation is considerably more complicated in case of multidimensional heterogeneity where I propose three different approaches to identification using results from transportation theory.
Working Papers
The Role of the Propensity Score in Fixed Effect Models (joint with Guido Imbens)
We develop a new estimator for the average treatment effect in the observational studies with unobserved cluster-level heterogeneity. We show that under particular assumptions on the sampling scheme the unobserved confounders can be integrated out conditioning on the empirical distribution of covariates and policy variable within the cluster. To make this result practical we impose a particular exponential family structure that implies that a low-dimensional sufficient statistic can summarize the empirical distribution. Then we use modern causal inference methods to construct a novel doubly robust estimator. The proposed estimator uses the estimated propensity score to adjust the familiar fixed effect estimator.
Work in Progress
Combining Experimental Evidence: The Transportation Approach (joint with Stefan Wager)
Finite Sample Properties of Two-step Estimators for Structural Econometric Models (joint with Evgeni Drynkin and Lanier Benkard)
Last Updated 2 Jul 2018