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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Research . 2022
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Fragility of the Marginal Treatment Effect

Authors: Devereux, Paul J.;

Fragility of the Marginal Treatment Effect

Abstract

Many interesting and important economic questions relate to the effects of binary treatments such as starting a college degree or participating in a job training program. The causal effects of these treatments are likely to be heterogeneous and recent research has emphasized the estimation of heterogeneous treatment effects, with a particular focus on Marginal Treatment Effects (MTEs). In this note, I describe why common methods of estimating MTEs of binary treatments can be very sensitive to omitted higher powers of covariates and demonstrate this using simple Monte Carlo simulations. I conclude by discussing approaches that may be useful for researchers to address this problem in practice.

Research Council of Norway

Country
Ireland
Related Organizations
Keywords

ddc:330, Monte-Carlo-Simulation, Kausalanalyse, Schätztheorie, Instrumental variables, C26, Marginal treatment effects, C01

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green