Gevorg Khandamiryan
About me
I am a third-year PhD in the economics department at UC Berkeley. My research interests include econometrics, networks and behavioral economics.
Previously, I earned MPhil from the University of Oxford, and B.A. in Economics and Applied Mathematics from UC Berkeley.
Work in Progress
This paper extends the Generalized Mundlak Estimator (GME) framework of Arkhangelsky and Imbens (2024) to settings with network interference. In a fixed effect setup group-level unconfoundedness is tackled by balancing statistics, which can include group-level averages of regressors, treatments, and their functions, such that it is sufficient to eliminate differences between groups. When units are connected in a logistic network formation model, network's degree sequence is a sufficient statistic for the unobserved heterogeneity, and the joint distribution of covariates and treatments belongs to an exponential family. Group unconfoundedness can be tackled by using this statistic to determine the group membership of each unit and construct balancing scores according to those clusters. A doubly robust AIPW estimator that uses these balancing statistics is consistent and has nice asymptotic properties, as illustrated in a simulation study.