It also applies to standardization. So, we show that simple models without random effects are not guaranteed sufficient to account for clustering in non-linear models, and especially when estimating counterfactual means (even in linear models).
04.09.2025 17:45 — 👍 1 🔁 0 💬 0 📌 0
Our paper focuses on AIPW. We show when there will be low coverage due to clustering (by center). You can account for it using fixed effects or random effects. However, fixed effects have bad performance due to overfitting when there are many small centers, which was the case in our example
03.09.2025 18:14 — 👍 4 🔁 0 💬 1 📌 0
It was an absolute honor to chair EuroCIM2025! Huge thanks to everyone who made it happen - especially Oliver Dukes and @svansteelandt.bsky.social for their incredible support. A big thanks also to all the participants, without whom this conference wouldn’t be possible.
11.04.2025 20:10 — 👍 20 🔁 3 💬 2 📌 0
Looking forward to EuroCIM next week, where I'm going to talk about how we can make rigorous causal inference more mainstream.
Anybody else who will be there 😊? www.eurocim.org
03.04.2025 12:54 — 👍 32 🔁 5 💬 4 📌 0
Only ONE WEEK left to register for EuroCIM. Don't miss your chance to join top experts in causal inference for insightful talks & networking. Secure your spot now! www.eurocim.org/registration...
25.03.2025 07:42 — 👍 0 🔁 1 💬 0 📌 0
The EuroCIM program is live! Explore the sessions, speakers, and schedule here: www.eurocim.org/program.html. Get ready for an exciting conference! 🎉
11.03.2025 08:53 — 👍 5 🔁 4 💬 0 📌 0
MD | PhD Student in Clinical Epidemiology @UniBasel. | Supervisor: Matthias Briel
#ClinicalTrials #RStats
research fellow @clscohorts.bsky.social
epidemiology, causal inference, methods
Heisenberg Professor for Biostatistics at the Department of Statistics, LMU München | causal inference - missing data - HIV
michaelschomaker.github.io
Biostatistician working on methodology at Novartis. Simulation studies, non-inferiority, missing data, estimands, covariate adjustment…
He/him
https://tpmorris.substack.com/
Personality psych & causal inference @UniLeipzig. I like all things science, beer, & puns. Even better when combined! Part of http://the100.ci, http://openscience-leipzig.org
Professor of biostatistics at the University of Oslo. Causal inference, survival/event history analysis, jmgran.github.io
Postdoctoral Research Associate, Statistical Laboratory, University of Cambridge
Associate professor at Columbia University
Epidemiology, causal inference, addiction medicine
https://kararudolph.github.io/
FWO postdoc | Experimental and theoretical psychologist. Interested in behaviour change, causal inference, and research reproducibility.
Prof of Biostatistics, UCL
Professor of Biostatistics and Epidemiology at the London School of Hygiene & Tropical Medicine.
Assistant Professor of Biostatistics, Boston University. Statistical genomics, genomic data science, open science, R programming https://lmweber.org/
Stanford Professor | Computational Health Economics & Outcomes | Fair Machine Learning | Causality | Statistics | Health Policy | Health Equity
drsherrirose.org
Lab manual: stanfordhpds.github.io/lab_manual
Personal account
Epidemiologist and science communicator | newsletter: epiellie.substack.com | cohost @casualinfer podcast | Causal inference for public health #epitwitter | Canadian in US 🇨🇦 | she/her/Dr
statistician at harvard med school • causal inference, machine learning, nonparametrics
alexluedtke.com
Professor of Biostatistics
Vanderbilt University School of Medicine
Expert Biostatistics Advisor
FDA Center for Drug Evaluation and Research
https://hbiostat.org https://fharrell.com
Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics; the world's first department of biostatistics.
Find us online: https://publichealth.jhu.edu/departments/biostatistics
Subscribe: https://mailchi.mp/jh/biostats-newsletter
Biostatistician, London School of Hygiene & Tropical Medicine. Blogging at thestatsgeek.com
