Using Machine Learning Methods to Support Causal Inference in Econometrics

Achim Ahrens, Christopher Aitken, Mark Edwin Schaffer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We provide an introduction to the use of machine learning methods in econometrics and how these methods can be employed to assist in causal inference. We begin with an extended presentation of the lasso (least absolute shrinkage and selection operator) of Tibshirani[50]. We then discuss the ‘Post-Double-Selection’ (PDS) estimator of Belloni et al.[13, 19] and show how it uses the lasso to address the omitted confounders problem. The PDS methodology is particularly powerful for the case where the researcher has a high-dimensional set of potential control variables, and needs to strike a balance between using enough controls to eliminate the omitted variable bias but not so many as to induce overfitting. The last part of the paper discusses recent developments in the field that go beyond the PDS approach.

Original languageEnglish
Title of host publicationBehavioral Predictive Modeling in Economics
PublisherSpringer
Pages23-52
Number of pages30
ISBN (Electronic)9783030497286
ISBN (Print)9783030497279
DOIs
Publication statusE-pub ahead of print - 6 Aug 2020
Event13th International Conference of the Thailand Econometric Society 2020 - Chiang Mai University, Chiang Mai, Thailand
Duration: 8 Jan 202010 Jan 2020

Publication series

NameStudies in Computational Intelligence
Volume897
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference13th International Conference of the Thailand Econometric Society 2020
Abbreviated titleTES2020
CountryThailand
CityChiang Mai
Period8/01/2010/01/20

Keywords

  • Causal inference
  • Lasso
  • Machine learning

ASJC Scopus subject areas

  • Artificial Intelligence

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