Abstract
We measure how accurately replication of experimental results can be predicted by a black-box statistical model. With data from four largescale replication projects in experimental psychology and economics, andtechniques from machine learning, we train a predictive model and study which variables drive predictable replication.The model predicts binary replication with a cross validated accuracy rate of 70% (AUC of 0:79) and relative effect size with a Spearman of 0:38. The accuracy level is similar to the market-aggregated beliefs of peer scientists (Camerer et al., 2016; Dreber et al., 2015). The predictive power is validated in a pre-registered out of sample test of the outcome of Camerer et al. (2018b), where 71% (AUC of 0:73) of replications are predicted correctly and effect size correlations amount to = 0:25. Basic features such as the sample and effect sizes in original papers,and whether reported effects are single-variable main effects or twovariable interactions, are predictive of successful replication. The models presented in this paper are simple tools to produce cheap, prognostic replicability metrics. These models could be useful in institutionalizing the process of evaluation of new findings and guiding resources to thosedirect replications that are likely to be most informative.