AAAI Publications, Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence

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Finding Exogenous Variation in Data
Eliot Abrams, George Gui, Ali Hortacsu

Last modified: 2018-06-20

Abstract


We reconsider the classic problem of recovering exogenous variation from an endogenous regressor. Two-stage least squares recovers the exogenous variation through presuming the existence of an instrumental variable. We instead rely on the assumption that the regressor is a mixture of exogenous and endogenous observations–say as the result of a temporary natural experiment. With this assumption, we propose an alternative two-stage method based on nonparametrically estimating a mixture model to recover a subset of the exogenous observations. We demonstrate that our method recovers exogenous observations in simulation and can be used to find pricing experiments hidden in grocery store scanner data.

Keywords


IV; Finite Mixture Models; Nonparametric Estimation

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