Guido Biele
@guidobiele.bsky.social
Bayesian stats, causal inference, child and youth mental health.
https://gbiele.github.io/
https://gbiele.github.io/
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.
"Researchers’ mission is to advance, innovate, and engage deeply in important problems; not report the median mediocrity and 'torrential outpouring of unchecked, but convincing-sounding ‘information’' (Guest et al., 2025) of what is found on the Internet."
I enjoyed writing this little blog post on why LLMs are incompatible with being a credible scholar. www.psychologytoday.com/ca/blog/how-...
July 23, 2025 at 4:12 PM
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.
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.
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.
June 5, 2025 at 4:09 PM
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.
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.
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?).
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?).
Let’s say you have very low sample response rate (recruit tens of millions and only tens of thousands self select into sample).
Then you have a RCT where there is clean random assignment of treatment.
Does the first stage sample selection bias your estimate of causal effect of treatment?
Then you have a RCT where there is clean random assignment of treatment.
Does the first stage sample selection bias your estimate of causal effect of treatment?
April 18, 2025 at 3:25 PM
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?).
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?).
Reposted by Guido Biele
Sometimes it's not only *what* is being said but *by whom* it is being said.
www.linkedin.com/feed/update/...
www.linkedin.com/feed/update/...
February 23, 2025 at 3:09 PM
Sometimes it's not only *what* is being said but *by whom* it is being said.
www.linkedin.com/feed/update/...
www.linkedin.com/feed/update/...
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?
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?
ADHD medication might improve learning much less than you would expect.
💊 Our new pretest-posttest control group design target trial emulation on the long-term effects of ADHD medication, together with Kristin Romvig Øvergaard, Arnoldo Frigessi & @guidobiele.bsky.social, has just been published. 🧵
💊 Our new pretest-posttest control group design target trial emulation on the long-term effects of ADHD medication, together with Kristin Romvig Øvergaard, Arnoldo Frigessi & @guidobiele.bsky.social, has just been published. 🧵
Long-term effect of pharmacological treatment on academic achievement of Norwegian children diagnosed with ADHD: a target trial emulation
AbstractBackground. Attention-deficit/hyperactivity disorder (ADHD) is one of the most commonly diagnosed mental disorders in children. For many patients,
academic.oup.com
February 21, 2025 at 6:36 PM
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?
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?
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.
February 17, 2025 at 6:07 PM
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.
There are no wins in causal inference, only trade-offs.
February 17, 2025 at 5:50 PM
There are no wins in causal inference, only trade-offs.
There are no wins in statistics, only trade-offs.
September 25, 2023 at 9:35 AM
There are no wins in statistics, only trade-offs.