Using Split Samples to Improve Inference About Causal Effects

33 Pages Posted: 12 Jan 2016

See all articles by Marcel Fafchamps

Marcel Fafchamps

Stanford University - Freeman Spogli Institute for International Studies

Julien Labonne

National University of Singapore (NUS) - Yale-NUS College

Date Written: January 2016

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.

Suggested Citation

Fafchamps, Marcel and Labonne, Julien, Using Split Samples to Improve Inference About Causal Effects (January 2016). NBER Working Paper No. w21842, Available at SSRN: https://ssrn.com/abstract=2713546

Marcel Fafchamps (Contact Author)

Stanford University - Freeman Spogli Institute for International Studies ( email )

Stanford, CA 94305
United States

Julien Labonne

National University of Singapore (NUS) - Yale-NUS College ( email )

Singapore

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