Treatment Effects: A Bayesian Perspective
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Treatment Effects : A Bayesian Perspective. / Heckman, James J.; Lopes, Hedibert F.; Piatek, Rémi.
I: Econometric Reviews, Bind 33, Nr. 1-4, 2014, s. 36-67.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Treatment Effects
T2 - A Bayesian Perspective
AU - Heckman, James J.
AU - Lopes, Hedibert F.
AU - Piatek, Rémi
N1 - JEL Classification; C11, C15, C31
PY - 2014
Y1 - 2014
N2 - This paper contributes to the emerging Bayesian literature on treatment effects. It derives treatment parameters in the framework of a potential outcomes model with a treatment choice equation, where the correlation between the unobservable components of the model is driven by a low-dimensional vector of latent factors. The analyst is assumed to have access to a set of measurements generated by the latent factors. This approach has attractive features from both theoretical and practical points of view. Not only does it address the fundamental identification problem arising from the inability to observe the same person in both the treated and untreated states, but it also turns out to be straightforward to implement. Formulae are provided to compute mean treatment effects as well as their distributional versions. A Monte Carlo simulation study is carried out to illustrate how the methodology can easily be applied.
AB - This paper contributes to the emerging Bayesian literature on treatment effects. It derives treatment parameters in the framework of a potential outcomes model with a treatment choice equation, where the correlation between the unobservable components of the model is driven by a low-dimensional vector of latent factors. The analyst is assumed to have access to a set of measurements generated by the latent factors. This approach has attractive features from both theoretical and practical points of view. Not only does it address the fundamental identification problem arising from the inability to observe the same person in both the treated and untreated states, but it also turns out to be straightforward to implement. Formulae are provided to compute mean treatment effects as well as their distributional versions. A Monte Carlo simulation study is carried out to illustrate how the methodology can easily be applied.
KW - Faculty of Social Sciences
KW - Bayesian
KW - Counterfactual distributions
KW - Potential outcomes
KW - Treatment effects
U2 - 10.1080/07474938.2013.807103
DO - 10.1080/07474938.2013.807103
M3 - Journal article
C2 - 24187431
VL - 33
SP - 36
EP - 67
JO - Econometric Reviews
JF - Econometric Reviews
SN - 0747-4938
IS - 1-4
ER -
ID: 80821325