# Antoine Chambaz

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.

I thank V. Beffara for his css file.

## Research and publications

I am a professor at Paris Descartes University, a member of MAP5, its applied mathematics laboratory. I am the head of the Statistics group since June 2018. 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.

### Research areas/interests

• Semiparametrics
• Targeted learning
• Reinforcement learning
• Biostatistics
• Applications to medicine, public health, automotive safety, linguistics
• Causal inference, statistical inference for variable importance
• Precision medicine
• Adaptive designs for randomized controlled trials

### Technical reports submitted

Collaborative targeted inference from continuously indexed nuisance parameter estimators, with C. Ju and M. J. van der Laan (2018) — submitted. link

Faster rates for policy learning, with A. Luedtke (2017) — submitted. link link

Two-stage, adaptive trial designs that modify both the population enrolled and the randomization probabilities, with B. Luber and M. Rosenblum (2017) — submitted. link

From contextual to global rankings by passive safety of generational classes of light vehicles, with Z. Ouni and C. Chauvel (2016) — submitted. link

Practical targeted learning from large data sets by survey sampling, with P. Bertail and E. Joly (2016) — submitted. link

Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits, with A. Luedtke and E. Kaufmann (2016) — submitted. 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) — submitted. link

### Publications

Simpson's paradox, a tale of causality, with I. Drouet and S. Memetea, to appear in the forthcoming special issue Causalité of the Journal de la Société Française de Statistique (2018). 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, to appear in Anaesthesia, Critical Care & Pain Medicine (2018). 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, to appear in Stat. Methods in Med. Res. (2017). 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, to appear as a book chapter in Targeted Learning in Data Science, by S. Rose and M. J. van der Laan (Springer, 2017). link

Online targeted learning for time series, with M. J. van der Laan and S. Lendle, to appear as a book chapter in Targeted Learning in Data Science, by S. Rose and M. J. van der Laan (Springer, 2017). link

Targeting a simple statistical bandit problem, with W. Zheng, to appear as a book chapter in Targeted Learning in Data Science, by S. Rose and M. J. van der Laan (Springer, 2017). link

Computationally fast targeted learning using adaptive survey sampling, with E. Joly and X. Mary, to appear as a book chapter in Targeted Learning in Data Science, by S. Rose and M. J. van der Laan (Springer, 2017). 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

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.

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).

Causality, a trialogue, with I. Drouet, J-C. Thalabard, Journal of Causal Inference, 2(2): 201--241, DOI:10.1515/jci-2013-0024 (2014). published English version and version française

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

### Technical reports

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

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).

### R packages

• 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

## Other stuff

### Editorial responsibility

I am a co-editor at the International Journal of Biostatistics and an associate editor for the Journal of Causal Inference. I have also been an associate editor for Scandinavian Journal of Statistics from 2016 to 2018.