The value added of machine learning to causal inference: evidence from revisited studies

Anna Baiardi and Andrea A Naghi

The Econometrics Journal, 2024, vol. 27, issue 2, 213-234

Abstract: SummaryA new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods aiming to connect the econometric theory on these methods with empirical economics. We focus on the double machine learning, causal forest, and generic machine learning methods, in the context of both average and heterogeneous treatment effects. We illustrate the implementation of these methods in a variety of settings and highlight the relevance and value added relative to traditional methods used in the original studies.

Keywords: Average treatment effects; causal inference; heterogeneous treatment effects; machine learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1093/ectj/utae004 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:27:y:2024:i:2:p:213-234.

Access Statistics for this article

The Econometrics Journal is currently edited by Jaap Abbring

More articles in The Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().