Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Dec 7;19(2):261-270.
doi: 10.1515/ijb-2022-0017. eCollection 2023 Nov 1.

Statistics, philosophy, and health: the SMAC 2021 webconference

Affiliations
Free article

Statistics, philosophy, and health: the SMAC 2021 webconference

Nicolas Savy et al. Int J Biostat. .
Free article

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.

Keywords: Bayesian statistics; artificial intelligence; biostatistics; causality; health; philosophy.

PubMed Disclaimer

References

    1. Mayo, D. Statistical inference as severe testing: how to get beyond the statistics wars . Cambridge: Cambridge University Press; 2018.
    1. 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 . - DOI
    1. van der Laan, MJ, Rose, S. Targeted learning: causal inference for observational and experimental data. Springer series in statistics . New York: Springer; 2011.
    1. Benkeser, D, van der Laan, MJ. The highly adaptive Lasso estimator. Proc Int Conf Data Sci Adv Anal 2016;2016:689–96.
    1. 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 . - DOI

Publication types

LinkOut - more resources