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Discussion Paper Details

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Title: Using Split Samples to Improve Inference on Causal Effects

Author(s): Marcel Fafchamps and Julien Labonne

Publication Date: January 2016

Keyword(s): Bonferroni correction, data mining, pre-analysis plan and publication bias

Programme Area(s): Development Economics

Abstract: We discuss a method aimed at reducing the risk that spurious results are published. Researchers send their datasets to an independent third party who randomly generates training and testing samples. Researchers perform their analysis on the former and once the paper is accepted for publication the method is applied to the latter and it is those results that are published. Simulations indicate that, under empirically relevant settings, the proposed method significantly reduces type I error and delivers adequate power. The method ? that can be combined with pre-analysis plans ? reduces the risk that relevant hypotheses are left untested.

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Bibliographic Reference

Fafchamps, M and Labonne, J. 2016. 'Using Split Samples to Improve Inference on Causal Effects'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=11077