Statistics, philosophy, and health: the SMAC 2021 webconference-Reference-Cited by-同舟云学术

Statistics, philosophy, and health: the SMAC 2021 webconference

Published:2022-12-07 Issue:0 Volume:0 Page:
ISSN:2194-573X
Container-title:The International Journal of Biostatistics
language:en
Short-container-title:

Author:

Savy Nicolas¹^ORCID,Moodie Erica EM²,Drouet Isabelle³,Chambaz Antoine⁴,Falissard Bruno⁵,Kosorok Michael R.⁶,Krakow Elizabeth F.⁷,Mayo Deborah G.⁸,Senn Stephen⁹,Van der Laan Mark¹⁰

Affiliation:

1. Toulouse Institute of Mathematics, University of Toulouse III and IFERISS FED 4142, University of Toulouse , Toulouse , France

2. Department of Epidemiology & Biostatistics , McGill University , Montréal , Québec , Canada

3. SND (UMR CNRS 8011) , Sorbonne Université , Paris , France

4. MAP5 (UMR CNRS 8145) , Université de Paris , Paris , France

5. CESP, INSERM U1018 , Université Paris-Saclay , Villejuif , France

6. Department of Biostatistics and Department of Statistics and Operations Research , University of North Carolina at Chapel Hill , Chapel Hill , NC , USA

7. Fred Hutchinson Cancer Research Center and University of Washington , Seattle , WA , USA

8. Department of Philosophy , Virginia Tech , Blacksburg , VA , USA

9. Consultant Statistician , Edinburgh , UK

10. Division of Biostatistics, School of Public Health , University of California , Berkeley , USA

Abstract

Abstract SMAC 2021 was a webconference organized in June 2021. The aim of this conference was to bring together data scientists, (bio)statisticians, philosophers, and any person interested in the questions of causality and Bayesian statistics, ranging from technical to philosophical aspects. This webconference consisted of keynote speakers and contributed speakers, and closed with a round-table organized in an unusual fashion. Indeed, organisers asked world renowned scientists to prepare two videos: a short video presenting a question of interest to them and a longer one presenting their point of view on the question. The first video served as a “teaser” for the conference and the second were presented during the conference as an introduction to the round-table. These videos and this round-table generated original scientific insights and discussion worthy of being shared with the community which we do by means of this paper.

Funder

INCa-Cancéropôle GSO – AAP 2020 Emergence of projects

Tremplin 2021 program of the University of Toulouse 3

the Fonds de Recherche du Québec, Santé

Publisher

Walter de Gruyter GmbH

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Link

https://www.degruyter.com/document/doi/10.1515/ijb-2022-0017/pdf

Reference13 articles.

1. Mayo, D. Statistical inference as severe testing: how to get beyond the statistics wars. Cambridge: Cambridge University Press; 2018.

2. Petersen, ML, van der Laan, MJ. Causal models and learning from data: integrating causal modeling and statistical estimation. Epidemiology 2014;25:418–26. https://doi.org/10.1097/ede.0000000000000078.

3. van der Laan, MJ, Rose, S. Targeted learning: causal inference for observational and experimental data. Springer series in statistics. New York: Springer; 2011.

4. Benkeser, D, van der Laan, MJ. The highly adaptive Lasso estimator. Proc Int Conf Data Sci Adv Anal 2016;2016:689–96.

5. van der Laan, MJ. A generally efficient targeted minimum loss based estimator based on the highly adaptive lasso. Int J Biostat 2017;13:10. https://doi.org/10.1515/ijb-2015-0097.