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

Harvard

Heckman, JJ, Lopes, HF & Piatek, R 2014, 'Treatment Effects: A Bayesian Perspective', Econometric Reviews, bind 33, nr. 1-4, s. 36-67. https://doi.org/10.1080/07474938.2013.807103

APA

Heckman, J. J., Lopes, H. F., & Piatek, R. (2014). Treatment Effects: A Bayesian Perspective. Econometric Reviews, 33(1-4), 36-67. https://doi.org/10.1080/07474938.2013.807103

Vancouver

Heckman JJ, Lopes HF, Piatek R. Treatment Effects: A Bayesian Perspective. Econometric Reviews. 2014;33(1-4):36-67. https://doi.org/10.1080/07474938.2013.807103

Author

Heckman, James J. ; Lopes, Hedibert F. ; Piatek, Rémi. / Treatment Effects : A Bayesian Perspective. I: Econometric Reviews. 2014 ; Bind 33, Nr. 1-4. s. 36-67.

Bibtex

@article{60945026f2304eecba6cc9348c004068,
title = "Treatment Effects: A Bayesian Perspective",
abstract = "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.",
keywords = "Faculty of Social Sciences, Bayesian, Counterfactual distributions, Potential outcomes, Treatment effects",
author = "Heckman, {James J.} and Lopes, {Hedibert F.} and R{\'e}mi Piatek",
note = "JEL Classification; C11, C15, C31",
year = "2014",
doi = "10.1080/07474938.2013.807103",
language = "English",
volume = "33",
pages = "36--67",
journal = "Econometric Reviews",
issn = "0747-4938",
publisher = "Taylor & Francis",
number = "1-4",

}

RIS

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