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Causal Inference and Impact Evaluation

Denis Fougere () and Nicolas Jacquemet ()

Economie et Statistique / Economics and Statistics, 2019, issue 510-511-512, 181-200

Abstract: [eng] This paper describes, in a non technical way, the main impact evaluation methods, both experimental and quasi experimental, and the statistical model underlying them. In the first part, we provide a brief survey of the papers making use of those methods that have been published by the journal Economie et Statistique / Economics and Statistics over the past fifteen years. In the second part, some of the most important methodological advances to have recently been put forward in this field of research are presented. To finish, we focus not only on the need to pay particular attention to the accuracy of the estimated effects, but also on the requirement to replicate evaluations, carried out by experimentation or quasi experimentation, in order to distinguish false positives from proven effects.

JEL-codes: C1 C2 C3 C54 (search for similar items in EconPapers)
Date: 2019
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https://www.insee.fr/en/statistiques/fichier/42531 ... ere_Jacquemet_EN.pdf (application/pdf)

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Working Paper: Causal Inference and Impact Evaluation (2019) Downloads
Working Paper: Causal Inference and Impact Evaluation (2019) Downloads
Working Paper: Causal Inference and Impact Evaluation (2019) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:nse:ecosta:ecostat_2019_510t_13

DOI: 10.24187/ecostat.2019.510t.1996

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