Causal Inference and Impact Evaluation

Denis Fougere and Nicolas Jacquemet ()

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Abstract: 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.

Keywords: Causal effects; Causal inference; Evaluation methods (search for similar items in EconPapers)
Date: 2019-12
Note: View the original document on HAL open archive server: https://hal.science/hal-02866828v1
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Citations: View citations in EconPapers (1)

Published in Economie et Statistique / Economics and Statistics, 2019, Special Issue 50th Anniversary, 510-511-512, pp.181-200. ⟨10.24187/ecostat.2019.510t.1996⟩

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Related works:
Journal Article: Causal Inference and Impact Evaluation (2019)
Working Paper: Causal Inference and Impact Evaluation (2019)
Working Paper: Causal Inference and Impact Evaluation (2019)
Working Paper: Causal Inference and Impact Evaluation (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:spmain:hal-02866828

DOI: 10.24187/ecostat.2019.510t.1996

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