Doubly Robust Estimation of Average Treatment Effects on the Treated through Marginal Structural Models
dc.contributor.author
Schomaker, Michael
dc.contributor.author
Baumann, Philipp F. M.
dc.date.accessioned
2023-05-16T07:21:05Z
dc.date.available
2023-05-15T16:52:38Z
dc.date.available
2023-05-16T07:21:05Z
dc.date.issued
2023
dc.identifier.issn
2767-3324
dc.identifier.other
10.1353/obs.2023.0025
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/612246
dc.identifier.doi
10.3929/ethz-b-000612246
dc.description.abstract
Some causal parameters are defined on subgroups of the observed data, such as the average treatment effect on the treated and variations thereof. We explain how such parameters can be defined through parameters in a marginal structural (working) model. We illustrate how existing software can be used for doubly robust effect estimation of those parameters. Our proposal for confidence interval estimation is based on the delta method. All concepts are illustrated by estimands and data from the data challenge of the 2022 American Causal Inference Conference.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Penn Press
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Average Treatment Effect on the Treated
en_US
dc.subject
Marginal Structural Models
en_US
dc.subject
Data Challenge
en_US
dc.title
Doubly Robust Estimation of Average Treatment Effects on the Treated through Marginal Structural Models
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2023-05
ethz.journal.title
Observational Studies
ethz.journal.volume
9
en_US
ethz.journal.issue
3
en_US
ethz.pages.start
43
en_US
ethz.pages.end
57
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.place
Philadelphia, PA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute::06336 - KOF FB Data Science und Makroökon. Meth. / KOF FB Data Science and Macroec. Methods
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute::06336 - KOF FB Data Science und Makroökon. Meth. / KOF FB Data Science and Macroec. Methods
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute
ethz.tag
KOF-Key-refereed
en_US
ethz.date.deposited
2023-05-15T16:52:38Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2024-02-02T23:11:57Z
ethz.rosetta.lastUpdated
2024-02-02T23:11:57Z
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true
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