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High dimensional methods and inference on structural and treatment effects

Authors: Alexandre Belloni , Victor Chernozhukov and Christian Hansen
Date: 21 November 2013
Type: cemmap Working Paper, CWP59/13
DOI: 10.1920/wp.cem.2013.5913


The goal of many empirical papers in economics is to provide an estimate of the causal or structural effect of a change in a treatment or policy variable, such as a government intervention or a price, on another economically interesting variable, such as unemployment or amount of a product purchased. Applied economists attempting to estimate such structural effects face the problems that economically interesting quantities like government policies are rarely randomly assigned and that the available data are often high-dimensional. Failure to address either of these issues generally leads to incorrect inference about structural effects, so methodology that is appropriate for estimating and performing inference about these effects when treatment is not randomly assigned and there are many potential control variables provides a useful addition to the tools available to applied economists.

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Now published:
Alexandre Belloni, Victor Chernozhukov and Christian Hansen March 2014, High-Dimensional Methods and Inference on Structural and Treatment Effects, Journal article, American Economic Association

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