Causal inference in economics and marketing

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques...

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Bibliographic Details
Published inProceedings of the National Academy of Sciences - PNAS Vol. 113; no. 27; pp. 7310 - 7315
Main Author Varian, Hal R.
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 05.07.2016
Subjects
Physical Sciences
Sackler on Drawing Causal Inference from Big Data
Social Sciences
marketing
economics
causal inference
machine learning
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Summary:This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.
Bibliography:ObjectType-Article-1
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Author contributions: H.R.V. wrote the paper.
Edited by Richard M. Shiffrin, Indiana University, Bloomington, IN, and approved May 25, 2016 (received for review May 28, 2015)
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1510479113