E. Amoros, J. Martin, and B. Laumon, Estimation de la morbidité routière, Bulletin épidémiologique hebdomadaire, vol.19, pp.157-160, 1996.

M. Asbridge, J. R. Brubacher, and H. Chan, Cell phone use and traffic crash risk: a culpability analysis, International Journal of Epidemiology, vol.42, pp.259-267, 2013.

M. Baiocchi, J. Cheng, and D. S. Small, Instrumental variable methods for causal inference, Statistics in Medicine, vol.33, p.000335772800011, 2014.

E. Bareinboim and J. Pearl, Controlling selection bias in causal inference, Proceedings of The Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.

, JMLR, vol.22, pp.100-108

E. Bareinboim and J. Tian, Recovering causal effects from selection bias, Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI, pp.3475-3481, 2015.

J. Benichou, A review of adjusted estimators of attributable risk, Statistical Methods in Medical Research, vol.10, pp.195-216, 2001.

J. Berkson, Limitations of the application of fourfold table analysis to hospital data, Biometrics Bulletin, vol.2, pp.47-53, 1946.

J. Brubacher, H. Chan, and M. Asbridge, Culpability analysis is still a valuable technique, International Journal of Epidemiology, vol.43, pp.270-272, 2014.

P. Bruzzi, S. Green, D. Byar, L. Brinton, and C. Schairer, Estimating the population attributable risk for multiple risk factors using case-control data, American Journal of Epidemiology, vol.122, pp.904-914, 1985.

P. Cummings, F. P. Rivara, C. M. Olson, and K. Smith, Changes in traffic crash mortality rates attributed to use of alcohol, or lack of a seat belt, air bag, motorcycle helmet, or bicycle helmet, united states, Injury Prevention, vol.12, pp.148-154, 1982.

V. Didelez, S. Kreiner, and N. Keiding, Graphical models for inference under outcome-dependent sampling, Statistical Science, vol.25, pp.368-387, 2010.

V. Didelez, S. Meng, and N. A. Sheehan, Assumptions of iv methods for observational epidemiology, Statistical Science, pp.22-40, 2010.

F. Elwert, Graphical causal models," in Handbook of causal analysis for social research, pp.245-273, 2013.

F. Elwert and C. Winship, Endogenous selection bias: the problem of conditioning on a collider variable, Annual Review of Sociology, p.40, 2014.

C. E. Frangakis and D. B. Rubin, Principal stratification in causal inference, Biometrics, vol.58, pp.21-29, 2002.

M. Glymour, Causal diagrams. modern epidemiology. edited by: Rothman kj, greenland s, lash tl, 2008.

S. Greenland, Quantifying biases in causal models: classical confounding vs collider-stratification bias, Epidemiology, vol.14, pp.300-306, 2003.

S. Greenland, J. Pearl, and J. M. Robins, Causal Diagrams for Epidemiologic Research, Epidemiology, vol.10, pp.37-48, 1999.

M. A. Hernán, S. Hernández-díaz, and J. M. Robins, A structural approach to selection bias, Epidemiology, vol.15, pp.615-625, 2004.

M. A. Hernán, S. Hernández-díaz, M. M. Werler, and A. A. Mitchell, Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology, American Journal of Epidemiology, vol.155, pp.176-184, 2002.

P. W. Holland, Statistics and Causal Inference, Journal of the American Statistical Association, vol.81, pp.945-960, 1986.

M. Lajous, A. Bijon, G. Fagherazzi, M. Boutron-ruault, B. Balkau et al., Body mass index, diabetes, and mortality in french women: explaining away a "paradox, Epidemiology, p.10, 2014.

B. Laumon, B. Gadegbeku, J. Martin, and M. Biecheler, Cannabis intoxication and fatal road crashes in france: population based case-control study, BMJ, vol.331, p.1371, 2005.

J. Martin, B. Gadegbeku, D. Wu, V. Viallon, and B. Laumon, Cannabis, alcohol and fatal road accidents, PLoS one, vol.12, p.187320, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01644331

J. S. Mill, A System of logic, ratiocinative and inductive, being a connected view of the principles of evidence, and the methods of scientific investigation, 1843.

K. L. Moore, R. Neugebauer, M. J. Laan, and I. B. Tager, Causal inference in epidemiological studies with strong confounding, Statistics in medicine, vol.31, pp.1380-1404, 2012.

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

J. Pearl, Causality: models, reasoning, and inference, 2000.

J. Pearl, Causal inference in statistics: An overview, Statistics Surveys, vol.3, pp.96-146, 2009.

K. Perchonok, Identification of specific problems and countermeasures targets for reducing alcohol related casualties, 1978.

T. S. Richardson and J. M. Robins, Single world intervention graphs (swigs): A unification of the counterfactual and graphical approaches to causality, Center for the Statistics and the Social Sciences, p.128, 2013.

M. D. Robertson and O. H. Drummer, Responsibility analysis: A methodology to study the effects of drugs in driving, Accident Analysis & Prevention, vol.26, pp.243-247, 1994.

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, pp.1393-1512, 1986.

J. M. Robins, M. A. Hernan, and B. Brumback, Marginal structural models and causal inference in epidemiology, Epidemiology, pp.550-560, 2000.

P. R. Rosenbaum and D. B. Rubin, The central role of the propensity score in observational studies for causal effects, Biometrika, vol.70, pp.41-55, 1983.

K. J. Rothman, S. Greenland, and T. L. Lash, Modern Epidemiology, 2008.

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

L. R. Salmi, L. Orriols, and E. Lagarde, Comparing responsible and non-responsible drivers to assess determinants of road traffic collisions: time to standardise and revisit, Injury Prevention, vol.20, pp.380-386, 2014.

P. Sanghavi, Commentary: Culpability analysis won't help us understand crash risk due to cell phones, International Journal of Epidemiology, vol.42, pp.267-269, 2013.

H. Smith and R. Popham, Blood alcohol levels in relation to driving, Canadian Medical Association Journal, vol.65, pp.325-328, 1951.

M. Sperrin, J. Candlish, E. Badrick, A. Renehan, and I. Buchan, Collider bias is only a partial explanation for the obesity paradox, Epidemiology, vol.27, pp.525-530, 2016.

J. Splawa-neyman, On the Application of Probability Theory to Agricultural Experiments, Essay on Principles. Section, vol.9, pp.465-472, 1990.

N. Stamatiadis and J. A. Deacon, Quasi-induced exposure: methodology and insight, Accident Analysis & Prevention, vol.29, pp.37-52, 1997.

K. Terhune, Problems and methods in studying drug crash effects, Alcohol, Drugs, and Driving, vol.2, 1986.

T. J. Vanderweele and J. M. Robins, Minimal sufficient causation and directed acyclic graphs, The Annals of Statistics, pp.1437-1465, 2009.

T. J. Vanderweele and J. M. Robins, Signed directed acyclic graphs for causal inference, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.72, pp.111-127, 2010.

T. J. Vanderweele and I. Shpitser, A new criterion for confounder selection, Biometrics, vol.67, pp.1406-1413, 2011.

T. J. Vanderweele and S. Vansteelandt, Odds Ratios for Mediation Analysis for a Dichotomous Outcome, American Journal of Epidemiology, vol.172, pp.1339-1348, 2010.

V. Viallon and M. Dufournet, Can collider bias fully explain the obesity paradox?, Epidemiology, 2017.

A. E. Wahlberg, The determination of fault in collisions, Driver Behaviour and Accident Research Methodology: Unresolved Problems, pp.101-120, 2009.

A. E. Wahlberg and L. Dorn, Culpable versus non-culpable traffic accidents; what is wrong with this picture?, Journal of Safety Research, vol.38, pp.453-459, 2007.