B. Ackerman, I. Schmid, K. E. Rudolph, M. J. Seamans, R. Susukida et al., Implementing statistical methods for generalizing randomized trial findings to a target population, Addictive Behaviors, vol.94, pp.124-132, 2019.

S. Athey, R. Chetty, G. Imbens, and H. Kang, The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely, 2019.

S. Athey, J. Tibshirani, and S. Wager, Generalized random forests, The Annals of Statistics, vol.47, issue.2, pp.1148-1178, 2019.

E. Bareinboim and J. Pearl, Causal inference and the data-fusion problem, Proceedings of the National Academy of Sciences, vol.113, issue.27, pp.7345-7352, 2016.

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.

A. L. Buchanan, M. G. Hudgens, S. R. Cole, K. R. Mollan, P. E. Sax et al., Generalizing evidence from randomized trials using inverse probability of sampling weights, J. R. Statist. Soc. A, 2018.

D. T. Campbell, Factors relevant to the validity of experiments in social settings., Psychological Bulletin, vol.54, issue.4, pp.297-312, 1957.

A. P. Cap, CRASH-3: a win for patients with traumatic brain injury, The Lancet, vol.394, issue.10210, pp.1687-1688, 2019.

J. R. Carpenter and M. G. Kenward, Missing data in randomised controlled trials: a practical guide, 2007.

V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen et al., Double machine learning for treatment and causal parameters, Cemmap Working Paper, Centre for Microdata Methods and Practice, 2016.

C. Cinelli and J. Pearl, Generalizing experimental results by leveraging knowledge of mechanisms, European Journal of Epidemiology, vol.3, issue.8, 2020.

W. G. Cochran, The effectiveness of adjustment by subclassification in removing bias in observational studies, Biometrics, vol.24, pp.295-313, 1968.

S. R. Cole and E. A. Stuart, Generalizing Evidence From Randomized Clinical Trials to Target Populations: The ACTG 320 Trial, American Journal of Epidemiology, vol.172, issue.1, pp.107-115, 2010.

J. Concato, N. Shah, and R. I. Horwitz, Randomized, Controlled Trials, Observational Studies, and the Hierarchy of Research Designs, New England Journal of Medicine, vol.342, issue.25, pp.1887-1892, 2000.

J. D. Correa, J. Tian, and E. Bareinboim, Identification of Causal Effects in the Presence of Selection Bias, Proceedings of the AAAI Conference on Artificial Intelligence, vol.33, issue.3, pp.2744-2751, 2019.

, Effects of tranexamic acid on death, disability, vascular occlusive events and other morbidities in patients with acute traumatic brain injury (CRASH-3): a randomised, placebocontrolled trial, The Lancet, vol.394, pp.1713-1723, 2019.

S. Cro, T. P. Morris, B. C. Kahan, V. R. Cornelius, and J. R. Carpenter, A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic, 2020.

I. J. Dahabreh, S. J. Haneuse, J. M. Robins, S. E. Robertson, A. L. Buchanan et al., Study designs for extending causal inferences from a randomized trial to a target population, 2019.

I. J. Dahabreh and M. A. Hernán, Extending inferences from a randomized trial to a target population, European Journal of Epidemiology, vol.34, issue.8, pp.719-722, 2019.

I. J. Dahabreh, S. E. Robertson, and M. A. Hernán, On the Relation Between G-formula and Inverse Probability Weighting Estimators for Generalizing Trial Results, Epidemiology, vol.30, issue.6, pp.807-812, 2019.

I. J. Dahabreh, S. E. Robertson, J. A. Steingrimsson, E. A. Stuart, and M. A. Hernán, Extending inferences from a randomized trial to a new target population, Statistics in Medicine, vol.39, issue.14, pp.1999-2014, 2020.

I. J. Dahabreh, S. E. Robertson, E. J. Tchetgen, E. A. Stuart, and M. A. Hernán, Generalizing causal inferences from individuals in randomized trials to all trial?eligible individuals, Biometrics, vol.75, issue.2, pp.685-694, 2019.

I. J. Dahabreh, S. E. Robertson, E. J. Tchetgen, E. A. Stuart, and M. A. Hernán, Generalizing causal inferences from individuals in randomized trials to all trial?eligible individuals, Biometrics, vol.75, issue.2, pp.685-694, 2019.

I. J. Dahabreh, J. M. Robins, and M. A. Hernán, Benchmarking Observational Methods by Comparing Randomized Trials and Their Emulations, Epidemiology, vol.31, issue.5, pp.614-619, 2020.

A. Deaton and N. Cartwright, Understanding and misunderstanding randomized controlled trials, Social Science & Medicine, vol.210, pp.2-21, 2018.

A. Deaton, S. C. Case, N. Côté, J. Drèze, W. Easterly et al., Introduction: Randomization in the Tropics Revisited, a Theme and Eleven Variations, Randomized Control Trials in the Field of Development, pp.29-46, 2020.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum Likelihood from Incomplete Data Via theEMAlgorithm, Journal of the Royal Statistical Society: Series B (Methodological), vol.39, issue.1, pp.1-22, 1977.

Y. Dewan, E. O. Komolafe, J. H. Mejía-mantilla, P. Perel, I. Roberts et al., CRASH-3 - tranexamic acid for the treatment of significant traumatic brain injury: study protocol for an international randomized, double-blind, placebo-controlled trial, Trials, vol.13, issue.1, p.6, 2012.

L. Dong, S. Yang, X. Wang, D. Zeng, and J. Cai, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

T. Frieden, Evidence for Health Decision Making ? Beyond Randomized, Controlled Trials, New England Journal of Medicine, vol.377, issue.5, pp.465-475, 2017.

L. W. Green and R. E. Glasgow, Evaluating the Relevance, Generalization, and Applicability of Research, Evaluation & the Health Professions, vol.29, issue.1, pp.126-153, 2006.

F. R. Guo and E. Perkovi?, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

R. Guo, L. Cheng, J. Li, P. R. Hahn, and H. Liu, A Survey of Learning Causality with Data, ACM Computing Surveys, vol.53, issue.4, pp.1-37, 2020.

P. R. Hahn, J. S. Murray, and C. M. Carvalho, Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects, 2020.

J. Hainmueller, Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies, Political Analysis, vol.20, issue.1, pp.25-46, 2012.

M. A. Hernán, B. C. Sauer, S. Hernández-díaz, R. Platt, and I. Shrier, Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses, Journal of Clinical Epidemiology, vol.79, issue.1, pp.70-75, 2016.

M. A. Hernán, The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data, American Journal of Public Health, vol.108, issue.5, pp.616-619, 2018.

M. A. Hernán and T. J. Vanderweele, Compound treatments and transportability of causal inference, Epidemiology, vol.22, issue.1, pp.368-77, 2011.

J. L. Hill, Bayesian nonparametric modeling for causal inference, Journal of Computational and Graphical Statistics, vol.20, issue.1, 2011.

P. Hünermund and E. Bareinboim, Preprint repository arXiv achieves milestone million uploads, Physics Today, vol.5, issue.1, 2014.

G. Imbens, Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics, National Bureau of Economic Research, issue.1, 2019.

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

W. Jiang, J. Josse, M. Lavielle, and T. Group, Logistic regression with missing covariates-parameter estimation, model selection and prediction within a joint-modeling framework, Computational Statistics & Data Analysis, vol.145, issue.2, 2020.

J. Josse, N. Prost, E. Scornet, and G. Varoquaux, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

N. Kallus, X. Mao, and M. Udell, Causal inference in sensorimotor integration, Advances in Neural Information Processing Systems 19, pp.6921-6932, 2007.

N. Kallus, A. M. Puli, and U. Shalit, Large Scale Hidden Semi-Markov SVMs, Advances in Neural Information Processing Systems 19, vol.2, p.3, 2007.

J. D. Kang and J. L. Schafer, Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data, Statistical Science, vol.22, issue.4, pp.523-539, 2007.

N. Keiding and T. A. Louis, Perils and potentials of self-selected entry to epidemiological studies and surveys, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol.179, issue.2, pp.319-376, 2016.

M. Kenward, The handling of missing data in clinical trials, Clinical Investigation, vol.3, issue.3, pp.241-250, 2013.

H. L. Kern, E. A. Stuart, J. Hill, and D. P. Green, Assessing Methods for Generalizing Experimental Impact Estimates to Target Populations, Journal of Research on Educational Effectiveness, vol.9, issue.1, pp.103-127, 2016.

S. R. Künzel, J. S. Sekhon, P. J. Bickel, and B. Yu, Metalearners for estimating heterogeneous treatment effects using machine learning, Proceedings of the National Academy of Sciences, vol.116, issue.10, pp.4156-4165, 2019.

S. R. Künzel, S. J. Walter, and J. S. Sekhon, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

S. Lê, J. Josse, and F. Husson, FactoMineR: AnRPackage for Multivariate Analysis, Journal of Statistical Software, vol.25, issue.1, pp.1-18, 2008.

J. Leigh, G. Collaborators, Y. Guo, K. Deribe, A. Brazinova et al., A collection of papers by J.J. Groen and his collaborators, Journal of Psychosomatic Research, vol.9, issue.2, p.234, 1965.

P. Forget, Faculty Opinions recommendation of Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015., The Lancet, vol.392, issue.7, pp.1859-1922, 2016.

P. Li and E. A. Stuart, Best (but oft-forgotten) practices: missing data methods in randomized controlled nutrition trials, The American journal of clinical nutrition, vol.109, issue.3, pp.504-508, 2019.

H. Linstone and M. Turoff, The Delphi Method: Techniques and Applications, vol.18, p.1, 1975.

S. Lodi, A. Phillips, J. Lundgren, R. Logan, S. Sharma et al., Effect estimates in randomized trials and observational studies: Comparing apples with apples, vol.188, p.5, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02481150

C. Louizos, U. Shalit, J. M. Mooij, D. Sontag, R. Zemel et al., Causal effect inference with deep latent-variable models, Advances in Neural Information Processing Systems, pp.6446-6456, 2017.

Y. Lu, D. O. Scharfstein, M. M. Brooks, K. Quach, and E. H. Kennedy, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

F. Martel-garcia and L. Wantchekon, Theory, external validity, and experimental inference: Some conjectures, The ANNALS of the American Academy of Political and Social Science, vol.628, issue.1, pp.132-147, 2010.

I. Mayer, J. Josse, N. Tierney, and N. Vialaneix, R-miss-tastic: a unified platform for missing values methods and workflows, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02879337

I. Mayer, E. Sverdrup, T. Gauss, J. Moyer, S. Wager et al., Doubly robust treatment effect estimation with missing attributes, Annals of Applied Statistics, vol.14, issue.3, pp.1409-1431, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02879332

, Missing Data in Clinical Trials, New England Journal of Medicine, vol.367, issue.26, pp.2557-2558, 2012.

J. Neyman, Sur les applications de la thar des probabilities aux experiences Agaricales: Essay de principle. English translation of excerpts by, T. Statistical Science, vol.5, issue.1, pp.465-472, 1923.

T. Q. Nguyen, B. Ackerman, I. Schmid, S. R. Cole, and E. A. Stuart, Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation details, PLOS ONE, vol.13, issue.12, p.e0208795, 2018.

T. Q. Nguyen, C. Ebnesajjad, S. R. Cole, and E. A. Stuart, Sensitivity analysis for an unobserved moderator in rct-to-target-population generalization of treatment effects, The Annals of Applied Statistics, vol.11, issue.1, pp.225-247, 2017.

X. Nie and S. Wager, Quasi-oracle estimation of heterogeneous treatment effects, 2017.

M. O'kelly and B. Ratitch, Clinical trials with missing data: a guide for practitioners, 2014.

M. Olschewski and H. Scheurlen, Comprehensive cohort study: an alternative to randomized consent design in a breast preservation trial, Methods of Information in Medicine, vol.24, issue.3, pp.131-134, 1985.

C. O'muircheartaigh and L. V. Hedges, Generalizing from unrepresentative experiments: a stratified propensity score approach, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.63, issue.2, pp.195-210, 2013.

J. Pearl, Causal diagrams for empirical research, Biometrika, vol.82, issue.4, pp.669-688, 1995.

J. Pearl, Causality (2 ed.), vol.5, 2009.

J. Pearl and E. Bareinboim, Transportability of Causal and Statistical Relations: A Formal Approach, 2011 IEEE 11th International Conference on Data Mining Workshops, pp.540-547, 2011.

J. Peters, D. Janzing, B. Schölkopf-;-e-peysakhovich, A. , and A. Lada, Combining observational and experimental data to find heterogeneous treatment effects, 2016.

S. Powers, J. Qian, K. Jung, A. Schuler, N. H. Shah et al., Some methods for heterogeneous treatment effect estimation in high dimensions, Statistics in Medicine, vol.37, issue.11, pp.1767-1787, 2018.

R. Prentice and G. Anderson, The women's health initiative: Lessons learned, Annual review of public health, vol.29, issue.1, pp.131-50, 2008.

. R-core-team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. 7.1.1, 2018.

M. Research, EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation, 2019.

T. S. Richardson and J. M. Robins, Micronesia (Federated States of), World Statistics Pocketbook (Ser. V), vol.128, pp.128-128, 2013.

J. Robins, A new approach to causal inference in mortality studies with a sustained exposure period?application to control of the healthy worker survivor effect, Mathematical Modelling, vol.7, issue.9-12, pp.1393-1512, 1986.

P. R. Rosenbaum and D. B. Rubin, Reducing bias in observational studies using subclassification on the propensity score, Journal of the American Statistical Association, vol.79, 1984.

K. J. Rothman, J. E. Gallacher, and E. E. Hatch, Why representativeness should be avoided, International Journal of Epidemiology, vol.42, issue.4, pp.1012-1014, 2013.

P. M. Rothwell, External validity of randomised controlled trials: ?To whom do the results of this trial apply??, The Lancet, vol.365, issue.9453, pp.82-93, 2005.

A. Rotnitzky and E. Smucler, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

D. Rubin and M. J. Van-der-laan, A Doubly Robust Censoring Unbiased Transformation, The International Journal of Biostatistics, vol.3, issue.1, 2007.

D. B. Rubin, Estimating causal effects of treatments in randomized and nonrandomized studies., Journal of Educational Psychology, vol.66, issue.5, pp.688-701, 1974.

D. B. Rubin, Inference and missing data, Biometrika, vol.63, issue.3, pp.581-592, 1976.

K. E. Rudolph and M. J. Van-der-laan, Robust estimation of encouragement design intervention effects transported across sites, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.79, issue.5, pp.1509-1525, 2016.

B. C. Saul and M. G. Hudgens, The Calculus of M-Estimation in R with geex, Journal of Statistical Software, vol.92, issue.2, pp.1-15, 2020.

S. Seaman and I. White, Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness, Communications in Statistics - Theory and Methods, vol.43, issue.16, pp.3499-3515, 2014.

W. R. Shadish, T. D. Cook, and D. T. Campbell, Experimental and quasi-experimental designs for generalized causal inference, 2002.

H. Shakur-still, I. Roberts, R. Bautista, J. Caballero, T. Coats et al., Effects of tranexamic acid on death, vascular occlusive events, and blood transfusion in trauma patients with significant haemorrhage (CRASH-2): a randomised, placebo-controlled trial, The Lancet, vol.376, issue.9734, pp.23-32, 2010.

A. Sharma and E. Kiciman, DoWhy: A Python package for causal inference, 2019.

E. A. Stuart, Matching Methods for Causal Inference: A Review and a Look Forward, Statistical Science, vol.25, issue.1, pp.1-21, 2010.

E. A. Stuart, B. Ackerman, and D. Westreich, Generalizability of Randomized Trial Results to Target Populations, Research on Social Work Practice, vol.28, issue.5, pp.532-537, 2017.

E. A. Stuart, C. P. Bradshaw, and P. J. Leaf, Assessing the Generalizability of Randomized Trial Results to Target Populations, Prevention Science, vol.16, issue.3, pp.475-485, 2014.

E. A. Stuart, S. R. Cole, C. P. Bradshaw, and P. J. Leaf, The use of propensity scores to assess the generalizability of results from randomized trials, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol.174, issue.2, pp.369-386, 2010.

M. Sugiyama and M. Kawanabe, Applications of Covariate Shift Adaptation, Machine Learning in Non-Stationary Environments, pp.137-179, 2012.

J. Textor, J. Hardt, and S. Knüppel, DAGitty, Epidemiology, vol.22, issue.5, p.745, 2011.

J. Tibshirani, S. Athey, and S. Wager, grf: Generalized Random Forests, 2020.

S. Tikka, A. Hyttinen, and J. Karvanen, Causal effect identification from multiple incomplete data sources: A general search-based approach, vol.3, 2019.

S. Tikka and J. Karvanen, Identifying Causal Effects with the R Package causaleffect, Journal of Statistical Software, vol.76, issue.12, 2017.

E. Tipton, Improving Generalizations From Experiments Using Propensity Score Subclassification, Journal of Educational and Behavioral Statistics, vol.38, issue.3, pp.239-266, 2013.

B. E. Twala, M. C. Jones, and D. J. Hand, Good methods for coping with missing data in decision trees, Pattern Recognition Letters, vol.29, issue.7, pp.950-956, 2008.

S. Van-buuren, Flexible Imputation of Missing Data, Second Edition, 2018.

J. Vandenbroucke, The HRT controversy: observational studies and RCTs fall in line, The Lancet, vol.373, issue.9671, pp.1233-1235, 2009.

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.

D. Westreich, J. K. Edwards, C. R. Lesko, E. Stuart, and S. R. Cole, Transportability of Trial Results Using Inverse Odds of Sampling Weights, American Journal of Epidemiology, vol.186, issue.8, pp.1010-1014, 2017.

J. Witte, L. Henckel, M. H. Maathuis, and V. Didelez, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

S. Yang, X. Wang, and D. Zeng, Preprint repository arXiv achieves milestone million uploads, Physics Today, vol.2, p.3, 2014.

S. Yang, D. Zeng, and X. Wang, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

L. Yao, Z. Chu, S. Li, Y. Li, J. Gao et al., Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

W. Zheng and M. J. Van-der-laan, Cross-Validated Targeted Minimum-Loss-Based Estimation, Targeted Learning, pp.459-474, 2011.