Using Split Samples to Improve Inference on Causal Effects

Marcel Fafchamps and Julien Labonne

No 11077, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Keywords: Bonferroni correction; Data mining; Pre-analysis plan; Publication bias (search for similar items in EconPapers)
JEL-codes: C12 C18 (search for similar items in EconPapers)
Date: 2016-01
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-exp
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Citations: View citations in EconPapers (10)

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Journal Article: Using Split Samples to Improve Inference on Causal Effects (2017)
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