M. H. Maathuis, M. Kalisch, and P. Bühlmann, Estimating high-dimensional intervention effects from observational data, Ann Stat, vol.37, issue.6 A, pp.3133-64, 2009.

P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction, and Search, 1993.

J. Pearl, Causality: Models, Reasoning, and Inference, 2009.

J. M. Robins, M. Á. Hernán, and B. Brumback, Marginal structural models and causal inference in epidemiology, Epidemiology, vol.11, pp.550-60, 2000.

M. A. Hernán, B. Brumback, and J. M. Robins, Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men, Epidemiology, vol.11, pp.561-70, 2000.

C. Robert, L. Thomas, I. Bondarenko, S. O'day, J. Weber et al., Ipilimumab plus Dacarbazine for previously untreated metastatic melanoma, N Engl J Med, vol.364, pp.2517-2543, 2011.

F. S. Hodi, S. J. O'day, D. F. Mcdermott, R. W. Weber, J. A. Sosman et al., Improved survival with Ipilimumab in patients with metastatic melanoma, N Engl J Med, vol.363, pp.711-734, 2010.

C. G. Drake, E. J. Lipson, and J. R. Brahmer, Breathing new life into immunotherapy: review of melanoma, lung and kidney cancer, Nat Rev Clin Oncol, vol.11, pp.24-37, 2014.

D. Schadendorf, F. S. Hodi, C. Robert, J. S. Weber, K. Margolin et al., Pooled analysis of long-term survival data from phase II and phase III trials of ipilimumab in unresectable or metastatic melanoma, J Clin Oncol, vol.33, pp.1889-94, 2015.

C. Garbe, T. K. Eigentler, U. Keilholz, A. Hauschild, and J. M. Kirkwood, Systematic review of medical treatment in melanoma: current status and future prospects, Oncologist, vol.16, pp.5-24, 2011.

D. J. Sargent, B. A. Conley, A. C. Collette, and L. , Clinical trial designs for predictive marker validation in cancer treatment trials, J Clin Oncol, vol.23, pp.2020-2027, 2005.

M. Buyse, S. Michiels, D. J. Sargent, A. Grothey, A. Matheson et al., Integrating biomarkers in clinical trials, Expert Rev Mol Diagn, vol.11, pp.171-82, 2011.

J. Pearl, Causal diagrams for empirical research, Biometrika, vol.82, pp.669-88, 1995.

M. Kalisch, B. A. Fellinghauer, E. Grill, M. H. Maathuis, U. Mansmann et al., Understanding human functioning using graphical models, BMC Med Res Methodol, vol.10, pp.10-14, 2010.

J. Pearl, Statistics and causal inference: a review, Test, vol.12, pp.281-345, 2003.

M. Kalisch, M. Machler, D. Colombo, M. H. Maathuis, P. Buhlmann et al., Causal inference using graphical models with the R package pcalg, J Stat Softw, vol.47, p.26, 2012.

M. H. Maathuis and P. Nandy, A review of some recent advances in causal inference. In handbook of big data, 2016.

D. Colombo and M. H. Maathuis, Order-independent constraint-based causal structure learning, J Mach Learn Res, vol.15, pp.1-40, 2014.

M. Kalisch and P. Buehlmann, Estimating high-dimensional directed acyclic graphs with the PC-algorithm, J Mach Learn Res, vol.8, pp.613-649, 2007.

N. Meinshausen and P. Bühlmann, Stability selection, J R Stat Soc Ser B Stat Methodol, vol.72, pp.417-73, 2010.

D. J. Stekhoven, I. Moraes, G. Sveinbjörnsson, L. Hennig, M. H. Maathuis et al., Causal stability ranking, Bioinformatics, vol.28, pp.2819-2842, 2012.

J. Pearl, Causal inference from indirect experiments, vol.7, 1995.

D. Firth, Bias reduction of maximum likelihood estimates, Biometrika, vol.80, pp.27-38, 1993.

G. Heinze and M. Schemper, A solution to the problem of separation in logistic regression, Stat Med, vol.21, pp.2409-2428, 2002.

I. Tsamardinos, L. E. Brown, and C. F. Aliferis, The max-min hill-climbing Bayesian network structure learning algorithm, Mach Learn, vol.65, pp.31-78, 2006.

S. Van-buuren and K. Groothuis-oudshoorn, MICE: multivariate imputation by chained equations in R, J Stat Softw, vol.45, pp.1-67, 2012.

K. Mohan, J. Pearl, and J. Tian, Graphical models for inference with missing data, Adv Neural Inf Process Syst, vol.26, pp.1-9, 2013.

T. Chu and C. Glymour, Search for additive nonlinear time series causal models, J Mach Learn Res, vol.9, pp.967-91, 2008.

K. H. Brodersen, F. Gallusser, J. Koehler, R. N. Scott, and S. L. , Inferring causal impact using bayesian structural time-series models, Ann Appl Stat, vol.9, pp.247-74, 2015.