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For each outcome, the conditional average treatment effect (CATE) of role model interventions, s 0 (Z), is predicted using five alternative ML methods: Elastic Net, Random Forest, Linear Model, Boosting, and Neural Network. The covariates Z that are used to predict the CATE consist of three indicators for the educational districts of Paris, Créteil, and Versailles, four indicators for students' socioeconomic background (high, medium-high, medium-low, and low), their age, their overall percentile rank in the Baccalauréat exam, their percentile ranks in the French and math tests of the exam, and a vector of 56 role model fixed effects. For each outcome, Panel A reports the parameter estimates and p-values (in square brackets) of the Best Linear Predictor (BLP) of the CATE using the best ML method (see Appendix Table N31, Panel A). The coefficients ? 1 and ? 2 correspond to the average treatment effect (ATE) and heterogeneity loading (HET) parameters in the BLP, respectively. Panel B reports the Sorted Group Average Treatment Effects (GATEs), Notes: This table reports heterogeneous treatment effects of the program on student outcomes for girls in Grade 12 (science track), using the methods developed by, 2018. ,
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