Welcome to my website. Here you will find a brief presentation of my past and current research, with links to my publications. Another page is dedicated to teaching.[Version française ici.] I thank V. Beffara for his css file.
Research and publications
I am a professor at Université Paris Cité, a member of MAP5, its applied mathematics laboratory. I am the head of the Statistics group since June 2018, and the director of the FP2M research federation. From 2012 to 2017, I was a member of Modal'X, the stochastic modelling laboratory of Paris Nanterre University. I chaired Modal'X from February 2014 to October 2017. My main research interest is in theoretical, computational and applied statistics.
- Targeted learning
- Reinforcement learning
- Applications to medicine, public health, automotive safety, linguistics
- Causal inference, statistical inference for variable importance
- Precision medicine
- Adaptive designs for randomized controlled trials
PhD students and postdoctorate fellows
- Herb Susmann (2023), avec E. Bacry et J. Josse
- Alexander Reisach (2022- ),
- Pan Zhao (2021- ), avec J. Josse
- Herb Susmann (2021-2022), avec L. Alkema
- Adrien Chevallier (2020- ), avec S. Martin
- Sandrine Boulet (2019-2023), avec A-S. Jannot et S. Zohar
- Thi Thanh Yen Nguyen (2018- ), avec O. Bouaziz et C. Neri
- Geoffrey Ecoto (2018- ), avec T. Cohignac (CCR)
- Samuel Doucet (2016-2019 - abandon), avec C. Chauvel (LAB)
- Emilien Joly (2016-2017), labex MME-DII, avec P. Bertail
- Emilien Joly (2015-2016), SPADRO
- Cabral Chanang (2012-2020), avec C. Butucea
- Zaïd Ouni (2013-2016), avec C. Chauvel (LAB)
- Wenjing Zheng (2012-2016), avec M. van der Laan
- Christophe Denis (2009-2012), avec A. Samson
Technical reports submitted
AdaptiveConformal: An R Package for Adaptive Conformal Inference, with H. Susmann and J. Josse (2023) — submitted. link
Quantile Super Learning for independent and online settings with application to solar power forecasting, with H. Susmann (2023) — submitted. link
Positivity-free Policy Learning with Observational Data, with P. Zhao, J. Josse and S. Yang (2023) — submitted. link
Inference in Marginal Structural Models by Automatic Targeted Bayesian and Minimum Loss-Based Estimation, with H. Susmann (2023) — submitted. link
Forecasting the cost of drought events in France by super learning, with G. Ecoto (2022) — submitted. link
One-step ahead sequential Super Learning from short time series of many slightly dependent data, and anticipating the cost of natural disasters, with G. Ecoto and A. F. Bibaut (2021) — submitted. See the technical report and companion R package SequentialSuperLearner.
Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data, with T. T. Y. Nguyen, W. Harchaoui, L. Mégret, C. Mendoza, O. Bouaziz, and C. Neri (2021) — submitted. link
Optimal tests of the composite null hypothesis arising in mediation analysis, with C. H. Miles (2021) — link
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models, with A. Reisach, M. Tami, C. Seiler, S. Weichwald, NeurIPS2023. link
Personalized online ensemble machine learning with applications for dynamic data streams, with I. Malenica, R. V. Phillips, A. Hubbard , R. Pirracchio, and M. J. van der Laan, Statistics in Medicine (2023). link
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning, with A. Bibaut, M. Dimakopoulos, N. Kallus and M. J. van der Laan (2021) — Proceedings of NeurIPS2021 (34). link
Post-Contextual-Bandit Inference, with A. Bibaut, M. Dimakopoulos, N. Kallus and M. J. van der Laan (2021) — Proceedings of NeurIPS2021 (34). link
Generalized policy elimination: an efficient algorithm for nonparametric contextual bandits, with A. Bibaut and M. J. van der Laan, Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), Proceedings of Machine Learning Research (PLMR), 124:1099-1108 (2020). PDF, supplementary PDF
L'échelle de la causalité: allégorie et formalisation, with V. Viallon, chapter 2 in the forthcoming book Causalité et statistique edited by F. Bertrand, G. Saporta and C. Thomas-Agnan (Éditions Technip, 2020).
Une brève introduction à l'apprentissage ciblé, with D. Benkeser, chapter 4 in the forthcoming book Causalité et statistique edited by F. Bertrand, G. Saporta and C. Thomas-Agnan (Éditions Technip, 2020). See the companion tlride website.
Simpson's paradox, a tale of causality, with I. Drouet and S. Memetea, special issue Causalité of the Journal de la Société Française de Statistique, 161(1):42--66 (2020). link
Causality: a special issue of Journal de la Société Française de Statistique (editorial), with D. Benkeser and M. J. van der Laan, special issue Causalité of the Journal de la Société Française de Statistique, 161(1):1--3 (2020). link
Performance guarantees for policy learning, with A. R. Luedtke, Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, 56(3):2162-2188 (2020). PDF
Prediction of an acute hypotensive episode during an ICU hospitalization with a super learner machine learning algorithm, with M. Cherifa, A. Blet, E. Gayat, M. Resche-Rigon and R. Pirracchio, Anesthesia & Analgesia, 130(5):1157--1166 (2020).
Biostatistics in Africa 2019: A special issue of The International Journal of Biostatistics, with A. Hubbard, A. R. Luedtke, and M. J. van der Laan, Int. J. Biostat., 15(2):1 (2019). link Foreword to a DGIJB special issue paying tribute to Africa’s statistical and epidemiological communities. link.
Big data and targeted machine learning in action to assist medical decision in the ICU: the past, the present and the future, with R. Pirracchio, J. Cohen, C. Lee, I. Malenica, M. Cannesson, M. Cohen, M. Resche-Rigon, and A. Hubbard, in Anaesthesia, Critical Care & Pain Medicine, 38(4): 377--384 (2019). link
Scalable collaborative targeted learning for high-dimensional data, with C. Ju, S. Gruber, S. Lendle, J. Franklin, R. Wyss, S. Schneeweiss and M. J. van der Laan, Stat. Methods in Med. Res., 28(2): 532--554 (2018). link
Contextual ranking by passive safety of generational classes of light vehicles, with Z. Ouni, C. Denis and C. Chauvel, J. R. Stat. Soc. Ser. C. Appl. Stat., 67(2): 395--416 (2018). link
C-TMLE for continuous tuning, with M. J. van der Laan and C. Ju, chapter 10 in Targeted Learning in Data Science, by S. Rose and M. J. van der Laan (Springer, 2018). link
Online targeted learning for time series, with M. J. van der Laan and S. Lendle, chapter 19 in Targeted Learning in Data Science, by S. Rose and M. J. van der Laan (Springer, 2018). link
Targeting a simple statistical bandit problem, with W. Zheng, chapter 24 in Targeted Learning in Data Science, by S. Rose and M. J. van der Laan (Springer, 2018). link
Targeted learning using adaptive survey sampling, with E. Joly and X. Mary, chapter 29 in Targeted Learning in Data Science, by S. Rose and M. J. van der Laan (Springer, 2018). link
Targeted sequential design for targeted learning inference of the optimal treatment rule and its mean reward, with W. Zheng and M. J. van der Laan, Ann. Statist., 45(6): 1--28 (2017). PDF
See also the companion supplemental article
Special issue on data-adaptive statistical inference, with A. Hubbard and M. J. van der Laan, Int. J. Biostat., 12 (1):1, DOI: 10.1515/ijb-2016-0033 (2016). link
Foreword to the DGIJB special issue on data-adaptive inference. link
Predicting is not explaining: targeted learning of the dative alternation, with G. Desagulier, Journal of Causal Inference, 4(1):1--30, DOI: 10.1515/jci-2014-0037 (2015). link
See also the slides of our presentation at the Language in Contrast conference.
tmle.npvi: targeted, integrative search of associations between DNA copy number and gene expression, accounting for DNA methylation, with P. Neuvial Bioinformatics, 31(18):3054--3056 (2015). link
See also the companion technical report, article and R package on CRAN.
Recension de l'ouvrage ``Big data. La révolution des données est en marche'' de V. Mayer-Schönberger et K. Cukier, with I. Drouet, Statistique et Société, 3(1):23--25 (2015). link to French text
Acceleration, due to occupational exposure, of time to onset of a disease, with C. Huber and D. Choudat, book chapter in Theory and practice of risk assessment, by C. Kitsos et al. (Springer Proceedings in Mathematics & Statistics 136, 2015).
Targeted Covariate-Adjusted Response-Adaptive LASSO-Based Randomized Controlled Trials, with M. J. van der Laan and W. Zheng, book chapter in Modern Adaptive Randomized Clinical Trials: Statistical, Operational, and Regulatory Aspects, by A. Sverdlov (CRC Press, 2015).
Analysis of the effect of occupational exposure to asbestos based on threshold regression modeling of case-control data, with D. Choudat, C. Huber, J-C Pairon, M. J. van der Laan, Biostatistics, 15(2): 327--340 (2014). link
Inference in targeted goup sequential covariate-adjusted randomized clinical trials, with M. J. van der Laan, Scand. J. Stat., 41(1):104--140, DOI:10.1111/sjos.12013 (2014). link
Estimation of a non-parametric variable importance measure of a continuous exposure, with P. Neuvial, M. J. van der Laan, Electron. J. Stat., 6:1059-1099 (2012). PDF.
See also the companion technical report, article, and our R package on CRAN.
Classification in postural style, with C. Denis, Ann. Appl. Stat., 6(3): 977--993 (2012). PDF
TMLE in Adaptive Group Sequential Covariate-Adjusted RCTs, with M. J. van der Laan, book chapter in Targeted Learning: Causal Inference for Observational and Experimental Data, by S. Rose and M. J. van der Laan (Springer, 2011).
Probability of Success of an In Vitro Fertilization Program, book chapter in Targeted Learning: Causal Inference for Observational and Experimental Data, by S. Rose and M. J. van der Laan (Springer, 2011).
Targeting the optimal design in randomized clinical trials with binary outcomes and no covariate: theoretical study, with M. J. van der Laan, Int. J. Biostat., 7(1), Article 10 (2011).
Targeting the optimal design in randomized clinical trials with binary outcomes and no covariate: simulation study, with M. J. van der Laan, Int. J. Biostat., 7(1), Article 11 (2011).
Deux modèles de Markov caché pour processus multiples et leur contribution à l'élaboration d'une notion de style postural, with I. Bonan, P-P. Vidal, Journal de la SFdS, 150(1): 28 pages (2009). PDF
A minimum description length approach to hidden Markov models with Poisson and Gaussian emissions. Application to order identification, with A. Garivier, E. Gassiat, J. Statist. Plann. Inference, 139(3): 962-977 (2009). link
Number of hidden states and memory: a joint order estimation problem for Markov chains with Markov regime, with C. Matias, ESAIM Probab. Stat., 13: 38-50 (2009). link
Control of neuronal persistent activity by voltage-dependent dendritic properties, with E. Idoux, D. Eugene, C. Magnani, J.A. White, L.E. Moore, J. Neurophysiol., 100: 1278-1286 (2008). link
Bounds for Bayesian order identification with application to mixtures, with J. Rousseau, Ann. Statist., 36(2): 938-962 (2008). PDF
Plica semilunaris temporal ectopia: an evidence of primary nasal pterygia traction, with E. Denion, P-H. Dalens, J. Petitbon, M. Gérard, Cornea, 26(3), pp. 769-777 (2007).
Testing the order of a model, Ann. Statist., 34(3): 1166-1203 (2006). PDF
Detecting abrupt changes in random fields, ESAIM: P&S, Novembre 2002, Vol. 6, pp. 189-209 (2002). link
Collaborative targeted inference from continuously indexed nuisance parameter estimators, with C. Ju and M. J. van der Laan (2018) — link
Two-stage, adaptive trial designs that modify both the population enrolled and the randomization probabilities, with B. Luber and M. Rosenblum (2017) — link
Practical targeted learning from large data sets by survey sampling, with P. Bertail and E. Joly (2016) — link
Data-adaptive Inference of the Optimal Treatment Rule and its Mean Reward. The Masked Bandit, with W. Zheng and M. J. van der Laan (2016). link
From contextual to global rankings by passive safety of generational classes of light vehicles, with Z. Ouni and C. Chauvel (2016) — link
Drawing valid targeted inference when covariate-adjusted response-adaptive RCT meets data-adaptive loss-based estimation, with an application to the LASSO, with W. Zheng and M. J. van der Laan (2015) — link
Targeted Covariate-Adjusted Response-Adaptive LASSO-Based Randomized Controlled Trials, with M. J. van der Laan and W. Zheng (2014). link
Targeted learning of the probability of success of an in vitro fertilization program controlling for time-dependent confounders, with S. Rose, J. Bouyer, M. J. van der Laan (2012). link
Estimation et test de l'ordre de lois, de l'importance de variables et de paramètres causaux; applications biomédicales, Habilitation à diriger des recherches, Université Paris Descartes (2011). PDF
Segmentation spatiale et sélection de modèle: théorie et applications statistiques, PhD thesis, Université Paris-Sud, Orsay (2003).
- SequentialSuperLearner (GitHub), implements the so-called overarching sequential super learning algorithm, a variant of the super learning algorithm. Designed to learn from times series, it sequentially identifies the best algorithm in a library, or the best combination of algorithms in the library, where the said library consists of several super learners, with G. Ecoto
- tmle.npvi (CRAN), Targeted Learning of a NP Importance of a Continuous Exposure, with P. Neuvial
- tsml.cara.rct (GitHub), Targeted Sequential Minimum Loss CARA RCT Design and Inference
- condensier (GitHub), Non-parametric conditional density estimation with binned conditional histograms, with O. Sofrygin, F. Blaauw, and M. J. van der Laan
- OnlineSuperLearner (GitHub), SuperLearner with online functionality for time-series analysis, with F. Blaauw
L'improviste, une lecture des stat'z (in French)
ANR Projects BOLD and SPADRO
INSERM workshop #209
I organized with M. Chavance the INSERM workshop #209 Recent advances in statistics for causal analysis. Check out the documents cordially made abailable by the speakers HERE.