M. L. Anderson, Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects, Journal of the American Statistical Association, issue.484, p.103, 2008.

N. Ashraf, N. Bau, C. Low, and K. Mcginn, Negotiating a Better Future: How Interpersonal Skills Facilitate Inter-generational Investment, Quarterly Journal of Economics, vol.135, issue.2, pp.1095-1151, 2020.

S. Athey, G. Imbens, ;. , and G. W. Imbens, Recursive Partitioning for Heterogeneous Causal Effects, Handbook of Economic Field Experiments, vol.113, pp.73-140, 2016.

F. Avvisati, M. Gurgand, N. Guyon, and É. Maurin, Getting Parents Involved: A Field Experiment in Deprived Schools, Review of Economic Studies, vol.81, issue.1, pp.57-83, 2014.
URL : https://hal.archives-ouvertes.fr/halshs-00942662

L. Babcock and S. Laschever, Women don't Ask: Negotiation and the Gender Divide, 2012.

A. V. Banerjee, E. Duflo, C. Imbert, and R. Pande, Entry, Exit and Candidate Selection: Experimental Evidence from India, 2013.

L. Beaman, E. Duflo, R. Pande, and P. Topalova, Female Leadership Raises Aspirations and Educational Attainment for Girls: A policy Experiment in India, Science, vol.335, issue.6068, pp.582-586, 2012.

D. Beede, T. Julian, D. Langdon, G. Mckittrick, B. Khan et al., Women in STEM: A Gender Gap to Innovation, Issue Brief, pp.4-11, 2011.

. Benjamini, A. M. Yoav, D. Krieger, and . Yekutieli, Adaptive Linear Step-up Procedures that Control the False Discovery Rate, Biometrika, vol.93, issue.3, pp.491-507, 2006.

M. Bertrand, B. Crépon, A. Marguerie, and P. Premand, Contemporaneous and Post-Program Impacts of a Public Works Program: Evidence from Côte d'Ivoire, 2017.

E. P. Bettinger and B. Long, Do Faculty Serve as Role Models? The Impact of Instructor Gender on Female Students, American Economic Review, vol.95, issue.2, pp.152-157, 2005.

D. E. Betz and D. Sekaquaptewa, My Fair Physicist? Feminine Math and Science Role Models Demotivate Young Girls, Social Psychological and Personality Science, vol.3, issue.6, pp.738-746, 2012.

D. A. Black, A. M. Haviland, S. G. Sanders, and L. J. Taylor, Gender Wage Disparities among the Highly Educated, Journal of Human Resources, vol.43, issue.3, pp.630-650, 2008.

F. D. Blau and L. M. Kahn, The Gender Wage Gap: Extent, Trends, and Explanations, Journal of Economic Literature, vol.55, issue.3, pp.789-865, 2017.

T. Breda and M. Hillion, Teaching Accreditation Exams Reveal Grading Biases Favor Women in Male-Dominated Disciplines in France, Science, vol.353, issue.6298, pp.474-478, 2016.
URL : https://hal.archives-ouvertes.fr/halshs-01379340

. Chernozhukov, 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.

M. L. Appendix-references-anderson, Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects, Journal of the American Statistical Association, issue.484, p.103, 2008.

S. Athey and G. W. Imbens, The Econometrics of Randomized Experiments, Handbook of Economic Field Experiments, vol.1, pp.73-140, 2017.

R. M. Baron and D. A. Kenny, The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations, Journal of Personality and Social Psychology, issue.6, pp.1173-1182, 1986.

D. Beede, T. Julian, D. Langdon, G. Mckittrick, B. Khan et al., Women in STEM: A Gender Gap to Innovation, Issue Brief, pp.4-11, 2011.

. Benjamini, A. M. Yoav, D. Krieger, and . Yekutieli, Adaptive Linear Step-up Procedures that Control the False Discovery Rate, Biometrika, vol.93, issue.3, pp.491-507, 2006.

E. Bloom, I. Bhushan, D. Clingingsmith, R. Hong, E. King et al., Contracting for Health: Evidence from Cambodia, 2006.

V. Chernozhukov, M. Demirer, E. Duflo, and I. Fernández-val, Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, 2018.

J. Cohen and P. Dupas, Free Distribution or Cost-Sharing? Evidence from a Randomized Malaria Prevention Experiment, Quarterly Journal of Economics, vol.125, issue.1, pp.1-45, 2010.

A. C. Davison and D. V. Hinkley, Bootstrap Methods and their Application, 1997.

E. Duflo and E. Saez, The Role of Information and Social Interactions in Retirement Plan Decisions: Evidence from a Randomized Experiment, The Quarterly Journal of Economics, vol.118, issue.3, pp.815-842, 2003.

P. Dupas, E. Huillery, and J. Seban, Risk Information, Risk salience, and Adolescent Sexual Behavior: Experimental Evidence from Cameroon, Journal of Economic Behavior & Organization, vol.145, pp.151-175, 2018.

R. A. Fisher, M. Frölich, and M. Huber, Direct and Indirect Treatment Effects-Causal Chains and Mediation Analysis with Instrumental Variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology, issue.5, pp.1645-1666, 1935.

T. Fujiwara and L. Wantchekon, Can Informed Public Deliberation Overcome Clientelism? Experimental Evidence from Benin, American Economic Journal: Applied Economics, vol.5, issue.4, pp.241-255, 2013.

M. Gayral-taminh, T. Matsuda, S. Bourdet-loubère, V. Lauwers-cances, J. Raynaud et al., Auto-évaluation de la qualité de vie d'enfants de 6 à 12 ans : construction et premières étapes de validation du KidIQol, outil générique présenté sur ordinateur, vol.17, pp.167-177, 2005.

J. Heckman, R. Pinto, and P. Savelyev, Understanding the Mechanisms through which an Influential Early Childhood Program Boosted Adult Outcomes, American Economic Review, issue.103, pp.2052-2086, 2013.

N. Ichino and M. Schündeln, Deterring or Displacing Electoral Irregularities? Spillover Effects of Observers in a Randomized Field Experiment in Ghana, The Journal of Politics, vol.74, issue.1, pp.297-307, 2012.

K. Imai, D. Tingley, T. Yamamoto-;-luke-keele, and T. Yamamoto, Identification, Inference and Sensitivity Analysis for Causal Mediation Effects, Journal of the Royal Statistical Society: Series A (Statistics in Society, vol.176, issue.1, pp.51-71, 2010.

G. W. Imbens and D. B. Rubin, Causal Inference in Statistics, Social, and Biomedical Sciences, 2015.

L. Keele, Causal Mediation Analysis: Warning! Assumptions Ahead, American Journal of Evaluation, vol.36, issue.4, pp.500-513, 2015.

M. Kuhn, Building Predictive Models in R using the caret Package, Journal of Statistical Software, vol.28, issue.5, pp.1-26, 2008.

D. P. Mackinnon, A. J. Fairchild, and M. S. Fritz, Mediation Analysis, Annual Review of Psychology, vol.58, pp.593-614, 2007.

J. A. Mcdonald and R. J. Thornton, Do New Male and Female College Graduates Receive Unequal Pay?, Journal of Human Resources, vol.42, issue.1, pp.32-48, 2007.

L. S. Paz and J. E. West, Should We Trust Clustered Standard Errors? A Comparison with Randomization-Based Methods, 2019.

P. R. Rosenbaum and . Studies, Design of Observational Studies, Springer Series in Statistics, 2002.

G. Vazquez-bare, Identification and Estimation of Spillover Effects in Randomized Experiments, 2018.

S. Wager and S. Athey, Estimation and Inference of Heterogeneous Treatment Effects using Random Forests, Journal of the American Statistical Association, vol.113, issue.523, pp.1228-1242, 2018.