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Aidan Coville · Study

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Aidan Coville

8 works· latest 2023· 2 institutionscluster: provisionalORCID 0000-0002-3437-6239

h-index5

i105

Citations198

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Most cited in this library

How do policymakers update their beliefs?2023 · 65 citations
No Household Left Behind: Afghanistan Targeting the Ultra Poor Impact Evaluation2019 · 55 citations
Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan2022 · 44 citations
How Often Should We Believe Positive Results? Assessing the Credibility of Research Findings in Development Economics2017 · 16 citations
Targeting Development Aid with Machine Learning and Mobile Phone Data2020 · 12 citations

Works

8 shown

How do policymakers update their beliefs?

Journal of Development Economics·2023·2 authors

65citations

Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan

Journal of Development Economics·2022·4 authors·Open access

44citations

Targeting Development Aid with Machine Learning and Mobile Phone Data

ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS)·2020·4 authors·other

12citations

Paying Attention to Profitable Investments: Experimental Evidence from Renewable Energy Markets

World Bank, Washington, DC eBooks·2019·3 authors·report·Open access

2citations

No Household Left Behind: Afghanistan Targeting the Ultra Poor Impact Evaluation

National Bureau of Economic Research·2019·5 authors·report

55citations

How Often Should We Believe Positive Results? Assessing the Credibility of Research Findings in Development Economics

·2017·2 authors·preprint·Open access

16citations

Trade-Offs and Risks in Results-Based Approaches

Springer eBooks·2016·2 authors·chapter

2citations

Lessons for Matching Grant Programs from Failed Attempts to Evaluate Them

World Bank, Washington, DC eBooks·2013·4 authors·book·Open access

2citations