A. Abbott, Transcending General Linear Reality, Sociological Theory, vol.6, issue.2, pp.169-186, 1988.
DOI : 10.2307/202114

A. Abbott, Chaos of Disciplines, 2001.
DOI : 10.7208/chicago/9780226001050.001.0001

S. Athey, Beyond prediction: Using big data for policy problems, Science, vol.355, issue.6324, pp.335-483, 2017.
DOI : 10.1145/2566486.2567967

C. Baudelot and R. Establet, Allez les filles. Une révolution silencieuse, 1992.

G. Biau and E. Scornet, A random forest guided tour, TEST, vol.4, issue.2, pp.197-227, 2016.
DOI : 10.1002/widm.1114
URL : https://hal.archives-ouvertes.fr/hal-01221748

J. L. Borges, The Total Library " , in Non-Fiction, pp.214-216, 1922.

L. Breiman, Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author), Statistical Science, vol.16, issue.3, pp.199-231, 2001.
DOI : 10.1214/ss/1009213726

B. Buchanan and . Ai-magazine, A (very) Brief History of Artificial Intelligence, pp.53-60, 2005.

C. Carpenter, Self-Reported Sexual Orientation and Earnings: Evidence from California, ILR Review, vol.58, issue.2, pp.258-273, 2006.
DOI : 10.2307/145924

A. Charpentier, E. Flachaire, J. Jakubowicz, and A. Ly, Econométrie et Machine Learning, 2017.

C. Chatfield, Model Uncertainty, Data Mining and Statistical Inference, Journal of the Royal Statistical Society. Series A (Statistics in Society), vol.158, issue.3, pp.419-466, 1995.
DOI : 10.2307/2983440

B. J. Copeland, What is Artificial Intelligence?, Alan Turing net archive, 2000.

D. Crevier, AI: The Tumultuous Search for Artificial Intelligence, 1993.

P. Domingos, The Master Algorithm. How the Quest for the Ultimate Machine Learning Will Remake Our World, 2015.

B. Efron and T. Hastie, Computer Age Statistical Inference, 2016.

J. Friedman, T. Hastie, and R. Tibshirani, Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, vol.33, issue.1, pp.1-22, 2010.
DOI : 10.18637/jss.v033.i01

C. Geertz, Thick Description. Toward an Interpretive Theory of Culture, The Interpretation of Cultures, 1973.

R. Grove, Three Eras of Survey Research, Public Opinion Quarterly, vol.75, issue.4, pp.861-871, 2011.
DOI : 10.1093/poq/nfr020

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009.

J. P. Ioannidis, Why Most Published Research Findings are False, Plos Medicine, vol.2, issue.8, 2005.

J. Mincer, National Bureau of Economic Research, 1974.

É. Ollion and J. Boelaert, Au-delà des big data. Les sciences sociales face à la multiplication des données numériques, Sociologie, vol.6, issue.3, pp.295-310, 2015.

, Open Science Collaboration Estimating the reproducibility of psychological science. A large-scale assessment suggests that experimental reproducibility in psychology leaves a lot to be desired, Science, vol.349, 2015.

J. Pearl, Causal inference in statistics: An overview, Statistics Surveys, vol.3, issue.0, pp.96-146, 2009.
DOI : 10.1214/09-SS057

G. Saporta, Probabilité, analyse de données et statistiques, 2006.

R. Silberzahn, E. L. Uhlmann, D. P. Martin, P. Anselmi, F. Aust et al., Many Analysts, One Dataset: Making Transparent How Variations in Analytical Choices Affect Results, 2017.
DOI : 10.31234/osf.io/qkwst

H. Varian, Big Data: New Tricks for Econometrics, Journal of Economic Perspectives, vol.28, issue.2, pp.3-28, 2014.
DOI : 10.1257/jep.28.2.3

R. L. Wasserstein and N. A. Lazar, -Values: Context, Process, and Purpose, The American Statistician, vol.50, issue.2, pp.129-133, 2016.
DOI : 10.1016/S0140-6736(10)61174-9

M. N. Wright and A. Ziegler, Journal of Statistical Software, vol.77, issue.1, pp.1-17, 2017.
DOI : 10.18637/jss.v077.i01