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Estimation of the Local Conditional Tail Average Treatment Effect
Journal of Business & Economic StatisticsThe conditional tail average treatment effect (CTATE) is defined as a difference between the conditional tail expectations (CTE’s) of potential outcomes, which can capture heterogeneity and deliver aggregated local information on treatment effects over different quantile levels and is closely related to the notion of second-order stochastic dominance ...
Chen, Le-Yu, Yen, Yu-Min
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The Impact of Measurement Error on Evaluation Methods Based on Strong Ignorability [PDF]
When selection bias can purely be attributed to observables, several estimators have been discussed in the literature to estimate the average effect of a binary treatment or policy on a scalar outcome.
Andrew Chesher, Erich Battistin
core
Dealing with Limited Overlap in Estimation of Average Treatment Effects [PDF]
Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions.
Guido W. Imbens +3 more
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Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand [PDF]
Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by lack of overlap in the covariate distributions.
Guido W. Imbens +3 more
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IDENTIFICATION AND INFERENCE FOR MARGINAL AVERAGE TREATMENT EFFECT ON THE TREATED WITH AN INSTRUMENTAL VARIABLE. [PDF]
Stat Sin, 2020 Liu L +4 more
europepmc +1 more source
AB0944 COMORBIDITIES IN THE SPONDYLOARTHRITIS GISEA COHORT: AN AVERAGE TREATMENT EFFECT ANALYSIS ON PATIENTS TREATED WITH BDMARDS [PDF]
, 2023 L. Scagnellato +6 more
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Local Instrumental Variables [PDF]
This paper unites the treatment effect literature and the latent variable literature. The economic questions answered by the commonly used treatment effect parameters are considered.
Edward J. Vytlacil, James J. Heckman
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Treatment Effect Heterogeneity in Theory and Practice [PDF]
Instrumental Variables (IV) methods identify internally valid causal effects for individuals whose treatment status is manipulable by the instrument at hand. Inference for other populations requires some sort of homogeneity assumption.
Joshua D. Angrist
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