📜 Reading list#
I spent a non-negligible part of my time opening new browser tabs with blog posts and papers luring to understand new concepts in machine learning, statistics, public health and social science. I never take the time to read them, but I keep adding them here. It increases the chance that I read them at some point.
Statistics#
Causal Inference#
Modern causal inference, Alejandro Schuler, 2024-WIP : Influence function stake. The pdf book seems in progress, the online version has more content in june 2024.
✅ Wager course on causal inference, Stats-361 : Super clear and quite accessible book on causal inference by Wager with a focus on econometrics.
Measurement bias and effect restoration in causal inference, Kuroki and Pearl, 2014 foundations on proxy learning.
The Hades toolbox for observational studies: All packages developped and maintained by Ohdsi
✅ The E-value, measuring the effect of potential unmeasured counfounder : A simple tool to measure what should be the effect of a confounder to shift the estimate.
Machine Learning#
factorization machine : A mix between matrix factorization and SVM for recommander systems.
Implicit Layer : Neural ODE stuffs, continuous process.
✅ Language Models: A guide for the Perplexed, 2023: un cours de Noah Smith de AI2.
Other statistical stuffs#
Geographical statistics: Introduction à la gémoatique poour le statisticien, Semecurbe, Coudin, 2022
Healthcare#
Sustainable healthcare#
Healthcare economy#
Enquêtes sur les bénéficiaires de l’expérimentation PEPS, 2024, Irdes
✅ Mousquès, J. (2023). Déserts médicaux en soins de premier recours: un regard économique. Les Tribunes de la santé, (4), 57-63.: Point d’entrée intéressant sur la relation offre demande en santé en France.
Sustainable technology#
[Murray Bookchin, vers une technologie libératrice](Vers une technologie libératrice)
Economy#
Sociology#
Hartmut Rosa, Accélération. Une critique sociale du temps
Harry M. Marks, The Progress of Experiment: Science and Therapeutic Reform in the United States