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Charly Marie, PhD

@charlymarie.bsky.social

Psychologist, working on the stigma of unemployment. (Re)lecteur assidu d’Astérix. https://charlymarie.github.io/

115 Posts200 Follower507 Folgt
Charly Marie, PhD@charlymarie.bsky.social · 4mo ago

A paper cited one of mine a few days ago (first time!). Great work: well thought out and written, good material, interesting identification strategy. Quite convincing. Then I checked my name in the references: “Marie, Coralie”… First reaction was to question if I could trust the paper. I don't.

anil oza@aniloza.bsky.social · 4mo ago

wow — the preprint host, arxiv, is banning authors for a year if they submit papers with hallucinated citations 🤖

Charly Marie, PhD@charlymarie.bsky.social · 5mo ago

Comes with a bonus point if you define your concept (very) loosely, so that it can be applied anywhere, anytime, for anything. Yes, I’m thinking about you, attitudes, the “general evaluation of an object”.

Julia M. Rohrer@dingdingpeng.the100.ci · 5mo ago

Psychology has a whole cottage industry in which people come up with some construct that is essentially "attitudes/beliefs/expectations/feelings about X", and then the central claim is that this construct is a super important determinant of future X outcomes.>

Charly Marie, PhD@charlymarie.bsky.social · 6mo ago

Automating code reviews is one area where LLMs could be highly useful. I still don't understand why papers are reviewed but code is not.

Michael Wiebe@michaelwiebe.bsky.social · 6mo ago

🚨Replication alert🚨 I'm pleased to announce that my replication of Moretti (2021) is now accepted as a comment at AER. I find ten issues in the paper. My comment focuses on two major problems; in the appendix, I document eight (relatively) minor problems. 1/

Charly Marie, PhD@charlymarie.bsky.social · 11mo ago

Great post, as always! I'd just add that multicollinearity is *almost* never an issue... unless your predictors are so heavily correlated that it becomes statistically spaghetti (i.e., that it becomes algebraically impossible to disentangle their unique effects, leading to unstable estimates).

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Julia M. Rohrer@dingdingpeng.the100.ci · 11mo ago

New blog post! Let's say you have two measures meant to capture the same confounder. They're highly correlated. Can you still proceed with your regression analysis? (I admit, the title is a bit of a spoiler) www.the100.ci/2025/10/13/i...

Jamie Cummins@jamiecummins.bsky.social · 12mo ago

This is when I realized: we’ve been here before! The garden of forking paths in well-trodden by us psychologists. And we already know that analytic flexibility can allow us to present basically any finding we want as significant. So I wondered: how much flexibility can we get with silicon samples?

Charly Marie, PhD@charlymarie.bsky.social · 12mo ago

LLMs are incredibly powerful tools. The only limitation is that, in order to unlock their full potential, it is necessary to verify the answer. In other words, you have to be able to do what the LLM is doing.

Kelly Bodwin@kellybodwin.com · 12mo ago

The thing that struck me most is that even if you take away the code-writing necessity, knowing what to ask the bot for requires so much expertise. Steps like "These points are overplotted, can we fix that" or "Let's look at only the top 50 restaurants" are incredible non-obvious to beginners...

Julien Gossa@juliengossa.cpesr.fr · 12mo ago

This would be the right time to push for the creation of an ‘old-school code school,’ proudly advertising that it doesn’t use AI — betting on the idea that programmers who actually know how to program will soon be rare and worth their weight in gold.

Charly Marie, PhD@charlymarie.bsky.social · 12mo ago

J’ai eu le plaisir d’intervenir à l'@unedic.bsky.social autour de la stigmatisation des "chômeurs" et des manières de composer (ou non) avec l’assignation sociale du chômage. Merci ! Un entretien prolonge la discussion : shorturl.at/5AFS4 (Avec notamment des bouts de @michaelzemmour.cpesr.fr)

Charly Marie : « Le chômage est une expérience de vie majoritaire et normale en France »

Charly Marie, docteur en psychologie, a travaillé sur les ressorts psychologiques des perceptions des personnes au chômage. Ses travaux éclairent la manière dont se construisent les stéréotypes au cœur de la stigmatisation des personnes privées d’emploi et ses effets sur les chômeurs et chômeuses.

unedic.org

Charly Marie, PhD@charlymarie.bsky.social · 12mo ago

@dingdingpeng.the100.ci and @vincentab.bsky.social wrote an amazing paper about marginal effects. While preparing my advanced statistics and methods class, I (surprisingly) had fun reproducing their Figure 1, adapting it to their Example 1, and ploting the text and arrows directly using ggplot().

Adaptation of Figure 1 from Rohrer & Arel-Bundock (2025), to their Example 1.
Julia M. Rohrer@dingdingpeng.the100.ci · last yr.

Ever stared at a table of regression coefficients & wondered what you're doing with your life? Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social) Preprint: doi.org/10.31234/osf... Website: j-rohrer.github.io/marginal-psy...

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).
easystats@easystats.github.io · last yr.

Okay, so you've crunched your numbers and got some awesome statistical models? Sometimes, just knowing "X predicts Y" isn't enough to really get to the juicy bits. That's where the cool post-hoc stuff comes in – think estimated marginal means, contrasts, pairwise comparisons, or #marginaleffects.