Using Impact Evaluation Tools to Unpack the Black Box and Learn What Works

Researchers and policy makers are increasingly dissatisfied with the “average treatment effect.” Not only are they interested in learning about the overall causal effects of policy interventions, but they want to know what specifically it is about the intervention that is responsible for any observ...

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Main Author: Laura R Peck
Format: Article
Language:English
Published: The Evaluation Center at Western Michigan University 2015-04-01
Series:Journal of MultiDisciplinary Evaluation
Subjects:
Online Access:https://journals.sfu.ca/jmde/index.php/jmde_1/article/view/415
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author Laura R Peck
author_facet Laura R Peck
author_sort Laura R Peck
collection DOAJ
description Researchers and policy makers are increasingly dissatisfied with the “average treatment effect.” Not only are they interested in learning about the overall causal effects of policy interventions, but they want to know what specifically it is about the intervention that is responsible for any observed effects. This discusses Peck's (2003) approach to creating symmetrically-predicted subgroups for analyzing endogenous features of experimentally evaluated interventions and then it identifies several possible extensions that might help evaluators better understand complex interventions. It aims to enrich evaluation methodologists’ toolbox, to improve our ability to analyze “what works” in addressing important questions for policy and program practice.
format Article
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publisher The Evaluation Center at Western Michigan University
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spelling doaj-art-ec35336a5ceb4c39a879b546a51029312025-08-20T01:51:10ZengThe Evaluation Center at Western Michigan UniversityJournal of MultiDisciplinary Evaluation1556-81802015-04-01112410.56645/jmde.v11i24.415Using Impact Evaluation Tools to Unpack the Black Box and Learn What WorksLaura R Peck0https://orcid.org/0000-0002-8516-9950Abt Associates Inc. Researchers and policy makers are increasingly dissatisfied with the “average treatment effect.” Not only are they interested in learning about the overall causal effects of policy interventions, but they want to know what specifically it is about the intervention that is responsible for any observed effects. This discusses Peck's (2003) approach to creating symmetrically-predicted subgroups for analyzing endogenous features of experimentally evaluated interventions and then it identifies several possible extensions that might help evaluators better understand complex interventions. It aims to enrich evaluation methodologists’ toolbox, to improve our ability to analyze “what works” in addressing important questions for policy and program practice. https://journals.sfu.ca/jmde/index.php/jmde_1/article/view/415experimental designmethodsblack box
spellingShingle Laura R Peck
Using Impact Evaluation Tools to Unpack the Black Box and Learn What Works
Journal of MultiDisciplinary Evaluation
experimental design
methods
black box
title Using Impact Evaluation Tools to Unpack the Black Box and Learn What Works
title_full Using Impact Evaluation Tools to Unpack the Black Box and Learn What Works
title_fullStr Using Impact Evaluation Tools to Unpack the Black Box and Learn What Works
title_full_unstemmed Using Impact Evaluation Tools to Unpack the Black Box and Learn What Works
title_short Using Impact Evaluation Tools to Unpack the Black Box and Learn What Works
title_sort using impact evaluation tools to unpack the black box and learn what works
topic experimental design
methods
black box
url https://journals.sfu.ca/jmde/index.php/jmde_1/article/view/415
work_keys_str_mv AT laurarpeck usingimpactevaluationtoolstounpacktheblackboxandlearnwhatworks