TY - GEN
T1 - Using Machine Learning Methods to Support Causal Inference in Econometrics
AU - Ahrens, Achim
AU - Aitken, Christopher
AU - Schaffer, Mark Edwin
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Causal inference
KW - Lasso
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85089911088&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-49728-6_2
DO - 10.1007/978-3-030-49728-6_2
M3 - Conference contribution
SN - 9783030497279
T3 - Studies in Computational Intelligence
SP - 23
EP - 52
BT - Behavioral Predictive Modeling in Economics
PB - Springer
T2 - 13th International Conference of the Thailand Econometric Society 2020
Y2 - 8 January 2020 through 10 January 2020
ER -