Donal Khosrowi
Extrapolation of causal effects – hopes, assumptions, and the extrapolator’s circle
Khosrowi, Donal
Authors
Abstract
I consider recent strategies proposed by econometricians for extrapolating causal effects from experimental to target populations. I argue that these strategies fall prey to the extrapolator’s circle: they require so much knowledge about the target population that the causal effects to be extrapolated can be identified from information about the target alone. I then consider comparative process tracing (CPT) as a potential remedy. Although specifically designed to evade the extrapolator’s circle, I argue that CPT is unlikely to facilitate extrapolation in typical econometrics and evidence-based policy applications. To argue this, I offer a distinction between two kinds of extrapolation, attributive and predictive, the latter being prevalent in econometrics and evidence-based policy. I argue that CPT is not helpful for predictive extrapolation when using the kinds of evidence that econometricians and evidence-based policy researchers prefer. I suggest that econometricians may need to consider qualitative evidence to overcome this problem.
Citation
Khosrowi, D. (2019). Extrapolation of causal effects – hopes, assumptions, and the extrapolator’s circle. Journal of Economic Methodology, 26(1), 45-58. https://doi.org/10.1080/1350178x.2018.1561078
Journal Article Type | Article |
---|---|
Publication Date | 2019 |
Deposit Date | Feb 8, 2019 |
Publicly Available Date | Feb 14, 2019 |
Journal | Journal of Economic Methodology |
Print ISSN | 1350-178X |
Electronic ISSN | 1469-9427 |
Publisher | Taylor and Francis Group |
Peer Reviewed | Peer Reviewed |
Volume | 26 |
Issue | 1 |
Pages | 45-58 |
DOI | https://doi.org/10.1080/1350178x.2018.1561078 |
Public URL | https://durham-repository.worktribe.com/output/1699919 |
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© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.