Arthur Chatton

Assistant professor in Biostatistics at Université de Montréal. Causal inference - Casual chess (or perhaps the reverse)

[I4R @I4Replication]

1/ Can AI help researchers check whether published social science results actually reproduce? In our new PNAS paper, we tested this directly in the AI Replication Games: 288 researchers, 103 teams, and real replication packages from quantitative social science.

[Andrew Vickers @VickersBiostats]

"LLMs .... overfit to surface patterns; struggle to abandon bad hypotheses even when evidence contradicts them; confuse correlation for causation; hallucinate explanations when experiments fail; optimize for plausibility, not truth". In short, LLMs act like human scientists.

[McGill School of Population and Global Health @McGill_SPGH]

We have a new Associate Dean and Director!
https://t.co/qcoRa1kuEN

[Alejandro Schuler @UnibusPluram]

do you guys know that you can *design* experiments with TMLE/DML/etc. in mind for the analysis? Here's the power calculation. Smaller sample size for free if you can guess how much your R2 will go down with ML adjustment.

https://t.co/t7KoI7lPnf

[Valeriy M., PhD, MBA, CQF @predict_addict]

The groundbreaking paper demonstrating that neural networks are often miscalibrated and tend to produce overconfident predictions has already garnered over 6,000 citations.

Despite this, some still occasionally make ludicrous claims on social media that 'calibration doesn’t matter.'

It’s important for the community to recognize the value of proper calibration to ensure reliable and trustworthy AI systems.

#calibration

[Achim Zeileis @[email protected] @AchimZeileis]

PSA: All #rstats package on #cran will get an official DOI!

This will facilitate bibliometrics and giving credit to R package authors.

Registering all 20,000+ packages will still take a few more days. But the first couple of thousand are already live. Example:

[Arthur Chatton @ArthurChatton]

https://t.co/Zcra1G83Rq

[Arthur Chatton @ArthurChatton]

Now published in AMPPS (link below, open access)! Some additions include a box on non-binary treatments, an expanded discussion on recent works for measurement errors, and a box on alternative forms of IPW. Thanks again to @dingding_peng, who does a fantastic job!

[Neuroskeptic 🇺🇦 @Neuro_Skeptic]

In 1599 the church used a placebo controlled trial to test if a French girl was possessed by a demon. Holy objects and identical, non-blessed objects were shown to the girl. "She reacted similarly when exposed to both genuine and sham religious objects" https://t.co/gi46ZR3ZE8

[Stat Society / Société Stat du Canada @SSC_stat]

CFIRB: Soumission d'abrégés pour les affiches jusqu'au 30 avril -- plusieurs prix de voyage à gagner ! / Submit a poster abstract by April 30th -- Several travel awards to win!
https://t.co/vAWOozPBsN

[Dr Kareem Carr @kareem_carr]

it's extremely powerful when all your projects fall under one overarching goal such that they feed into and enhance each other.

[Triad sou. @triadsou]

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data. Florie Bouvier, Etienne Peyrot, Alan Balendran, Corentin Ségalas, Ian Roberts, François Petit, Raphaël Porcher. Statistics in Medicine. https://t.co/CcacToIQj1

[Iván Díaz @ildiazm]

Testing for assumptions is not great practice. The test will lead to model changes if the assumption is violated, ruining the validity of uncertainty quantitfication procedures (e.g., CIs) We should use models with assumptions that are reasonably known to be correct a priori.

[Gary Collins @GSCollins]

All 3 papers (#openaccess) now out in the @bmj_latest on the **Evaluation of clinical prediction models**

part 1: https://t.co/vzcu6CXPCi

part 2: https://t.co/TXPfMLmi1r

part 3: https://t.co/yZ0J1VYp6d

#stats #MachineLearning #predictiveanalytics

[TRIPODStatement @TRIPODStatement]

Good news TRIPOD+AI #reporting recommendations for prediction model studies using either regression or #machinelearning methods has been accepted for publication 🥳🎉

#ArtificialInteligence #transparency #reportingstandards #standardsforAI

[🄼🄴🄴🄷🄰🅆🄻 ⭕ @meehawl]

Three vaccines (MMR, Flu, and HPV) and their risks, compared against becoming infected with the disease itself.

https://t.co/XQVGmoltpL

[Miguel Hernán @_MiguelHernan]

"Draw your assumptions before your conclusions"

5 years ago we launched the *free* #CausalDiagrams course @HarvardOnline @edxOnline.

Since then, 80,000 people in 180 countries have registered.

Check it out if you want to learn about DAGs and SWIGs :
https://t.co/nI6O3NfURW

[Björn Siepe @b_siepe]

We reviewed 100 psych. simulation studies & find room for improvement in planning/reporting. As a remedy, we (@BartosFra, @tmorris_mrc, @BoulesteixLaure, @Daniel_W_Heck & Samuel Pawel) present ADEMP-PreReg, a sim study preregistration & reporting template
https://t.co/kmWdgydo5F

[CAUSALab @CAUSALab]

Mark your calendars!

The 16th #KolokotronesSymposium takes place 12/15 in-person at @HarvardChanSPH and virtually.

Join us for a @CAUSALab & @h4p_research event. Learn about #causalinference in #pregnancy.

Registration opens soon - stay tuned!

[UCL Innovative Clinical Trials Unit @innovativectu]

What should you do when your simulation study results aren't what you expected?🤔

Following advice from @IanWhit25399993, @t_mpham, @stats_q & @tmorris_mrc can help you avoid errors and improve the quality of your studies.

Read now in @IntJEpidemiol👇https://t.co/uVNJFSCA47