Doubly Robust Estimation of Average Treatment Effects on the Treated through Marginal Structural Models
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Author / Producer
Date
2023
Publication Type
Journal Article
ETH Bibliography
yes
Citations
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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.
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Publication status
published
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Editor
Book title
Journal / series
Volume
9 (3)
Pages / Article No.
43 - 57
Publisher
Penn Press
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Average Treatment Effect on the Treated; Marginal Structural Models; Data Challenge
Organisational unit
06336 - KOF FB Data Science und Makroökon. Meth. / KOF FB Data Science and Macroec. Methods
02525 - KOF Konjunkturforschungsstelle / KOF Swiss Economic Institute