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Guido Biele

@guidobiele.bsky.social

163 followers 297 following 48 posts

Bayesian stats, causal inference, child and youth mental health. gbiele.github.io

Guido Biele @guidobiele.bsky.social · 23/07/2025

The last few days a couple of texts made the round here that equate using AI in the research process with doing bad research. I think these are 2 orthogonal things: Using AI can make research worse and it can make it better, depent on how it is used.

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Guido Biele @guidobiele.bsky.social · 05/06/2025

I believe large language models (LLMs) can be very useful and can be used to support learning effectively. However, this plot from a recent study (www.nature.com/articles/s41...), which praises the impact of LLMs on learning outcomes, doesn't exactly inspire confidence.

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Guido Biele @guidobiele.bsky.social · 18/04/2025

Agreed that selection bias receives too little attention. Still, the problem only materialises if the estimand is the effect in a target population. Then, a sensible thing to do is to draw a missingnes graph (m-DAG) and check if (conditional) exchangebility is violated (any backdoor paths?).

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Reposted by Guido Biele

Falk Steiner @fsteiner.bsky.social · 23/02/2025

Sometimes it's not only *what* is being said but *by whom* it is being said. www.linkedin.com/feed/update/...

Yann LeCunYann LeCun
VP & Chief AI Scientist at Meta
in a linkedin post:

Hey Europe, you want a vibrant tech industry, right?
The US seems set on destroying its public research funding system.
Many US-based scientists are looking for a Plan B.
You may have an opportunity to attract some of the best scientists in the world.

Scientists will go where they have the means to be the most creative and productive.
Here are the criteria that attract them:
1- access to top students and junior collaborators.
2- access to research funding with little administrative overhead.
3- good compensation (comparable with top universities in the US, Switzerland, Canada).
4- freedom to do research on what they think is most promising.
5- access to research facilities (e.g. computing infrastructure, etc).
6- ability to collaborate/consult with industry and startups.
7- moderate teaching and administrative duties.

They will seek the best trade-off between these criteria in academia, public research, or industry.

European academia rates high on 1 and 4, low on 2 (even if you can get an ERC grant), 5, 6 and 7, and *very* low on 3.
European industry rates low on almost every criterion, particularly on 4, but also on 3 and 5 compared to top US industry labs.

To attract the best scientific and technological talents, make science and technology research professions attractive.
It's pretty straightforward.

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Guido Biele @guidobiele.bsky.social · 21/02/2025

Causal inference is hard. Here you can see how we, really @tvarnetperez.bsky.social, do our best to give a good answer to an important question for those with ADHD: Does longterm medication improve learning outcomes?

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Guido Biele @guidobiele.bsky.social · 17/02/2025

Does anyone know of good resources for staying updated on large language model developments (ChatGPT, Gemini, Claude) relevant to quantitative researchers? I'm looking for feeds/accounts that describe experiences with models or highlight new releases (including betas) and other research updates.

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Guido Biele @guidobiele.bsky.social · 17/02/2025

There are no wins in causal inference, only trade-offs.

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Guido Biele @guidobiele.bsky.social · 25/09/2023

There are no wins in statistics, only trade-offs.

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