New study: Small benefits and risks of COVID-19 vaccines in children in Madrid. Hospitalization risk very low in unvaccinated, lower in vaccinated. 6-11 years old: no myocarditis cases 12-17 years old: myocarditis risk very low in vaccinated, lower in unvaccinated journals.lww.com/pidj/fulltex...

You're invited! ✉️ 20th Kolokotrones Symposium: “Acetaminophen During Pregnancy and Autism: What Does Causal Inference Take?" Details in comments. In-person limited to Harvard ID holders due to space restrictions. Online attendance free & public. Register: www.eventbrite.com/e/acetaminop...

Free Causal Inference Consulting Available at Harvard T.H. Chan School of Public Health! Take advantage of expert advice for your research projects. Learn more and help spread the word! :)

When using observational data for #causalinference, the choice isn’t between emulating or not emulating a #TargetTrial, but between reporting or not reporting the target trial that we are emulating. For those who prefer to be explicit about what they do, we have developed the TARGET Statement 👇

SER 2025 @societyforepi.bsky.social included a session spotlighting James M. Robins⭐ "Celebrating James M. Robins Contributions to Epidemiology" explored Robins' impact, including his landmark 1986 paper. It concluded with his comments on progress still to come in #causalinference research.

New publication: Effect of colonoscopy screening on risks of colorectal cancer and related death: instrumental variable estimation of per-protocol effects now in the European Journal of Epidemiology ➡️ Read here: link.springer.com/article/10.1... #cancer #screening #colorectal

See you in Madrid? CAUSALab is partnering w/ @cemfi.es for the course, Causal Inference for Health and Social Scientists. 📆 Aug 25-29, 2025 Taught by @miguelhernan.org, CEMFI course introduces 2 step causal framework for experimental & non-experimental data. www.cemfi.es/programs/css...

If you're wondering about differences between publicly-funded research in non-profit universities and privately-funded research in for-profit companies, watch this: www.youtube.com/watch?v=Ar0z... The topic is the "de-extinction of the dire wolf", but the message applies beyond it. (Think "AI".)

Barbra Dickerman, @joy-shi.bsky.social, and I have a new online course for anyone who wants to learn the basics of confounding adjustment for time-fixed treatments. A must if you are considering CAUSALab's "Advanced Confounding Adjustment" course for time-varying treatments in the Summer.

Anyone interested in science in the U.S. should read this. www.insidehighered.com/opinion/view...

Join us on Wednesday, March 5th at 1:00pm EST for the Department's seminar series with Miguel Hernan speaking on "How to make people immortal and why it is not a good idea: Improving the causal analyses of healthcare databases" ➡️ Go to event page to register: hsph.harvard.edu/epidemiology...

1/ When using observational data for #causalinference, emulating a target trial helps solve some problems... but not all problems. In a new paper, we explain why and when the #TargetTrial framework is helpful. www.acpjournals.org/doi/10.7326/... Joint work with my colleagues @causalab.bsky.social

1/ If you were taught to test for proportional hazards, talk to your teacher. The proportional hazards assumption is implausible in most #randomized and #observational studies because the hazard ratios aren't expected to be constant during the follow-up. So "testing" is futile. But there is more 👇

1/ That "immortal time" is so frequent in survival analyses for #causalinference is fascinating. Because "immortal time" doesn't exist in the data, *we* create it when misanalyzing the data. Our new paper pubmed.ncbi.nlm.nih.gov/39494894/ summarizes why immortal time arises & how to prevent it.

Upgrade your #causalinference arsenal. A revision of our book "Causal Inference: What If" is available at miguelhernan.org/whatifbook Thanks to everyone who suggested improvements, reported typos, and proposed new citations and material. Enjoy the #WhatIfBook plus code and data. Also, it's free.

Does #randomization ensures balance of risk factors between groups? Consider this: In Denmark 860 individuals were randomly allocated to either intervention or control. Individuals were unaware of their allocation. No intervention took place. Mortality was higher in the intervention group (p=0.003)

It's always a good time to remember Brandolini's principle @ziobrando.bsky.social

Join us today!

I enjoyed 'The Hazards of Hazard Ratios' by @miguelhernan.bsky.social! journals.lww.com/epidem/fullt...

Congrats to Roger Logan on his retirement! Roger has worked as a CAUSALab Senior Research Scientist @harvardchanschool.bsky.social for 23 years. He has been a valuable team member & made major contributions to #causalinference research. Wishing Roger all the best in this new chapter! #publichealth

Really enjoyed @miguelhernan.bsky.social's talk on the promises and limitations of AI for health data research at the #WCE2024! A fair and critical dose of reality that the wider health research sphere desparately needs to hear! #EpiSky

New research published in Annals of Internal Medicine challenges past scholarship on metformin. CAUSALab collaborator Yu-Han Chiu identified no increased risk for childbirth with major birth defects when compared w/ women who discontinued the drug. CNN article: www.cnn.com/2024/06/17/h...

This week I discussed methods for health technology assessment at HTAi. My main point: "Observational data (#RWD) can often be used to emulate a #TargetTrial, but we need more research to characterize questions that can only be answered by randomized trials." Let's learn the limits of #RWE.

Did you know that the LATE estimator was independently described in 1994 by Imbens & Angrist in Econometrica and Baker & Lindeman in Statistics in Medicine? onlinelibrary.wiley.com/doi/10.1002/... A delightful historical overview of LATE is now available www.tandfonline.com/doi/full/10....

With #TargetTrial emulation becoming increasingly popular, it's important to understand what it can and can't do. In this podcast I discuss how target trial emulation can improve causal inference from observational data and extend inferences from randomized trials edhub.ama-assn.org/jn-learning/...

First FEP-CAUSAL collab paper is out in AJE! pubmed.ncbi.nlm.nih.gov/38576166/ Target trial emulation findings support aripiprazole & paliperidone as first-line therapy in first episode psychosis treatment. Led by CAUSALab researchers Alejandro Szmulewicz & @miguelhernan.bsky.social.

CAUSALab has a new collaboration with Broad Institute of MIT & Harvard! @miguelhernan.bsky.social received a Multidisciplinary University Research Initiative (MURI) award as a project collaborator to advance intervention design decision-making via computational framework.

Don't let the causal inference buzz fool you: Description is the foundation of science. We've described the 3-year health impact of #COVID19 in Madrid, the EU region with the highest life expectancy. If we can't describe, no causal inference can follow. academic.oup.com/ofid/article...

Have you ever been advised to state your hypothesis in your grant application? Sander Greenland and I argue that stating hypotheses is unnecessary. lnkd.in/eW4ccgae Who cares what you guess the qualitative answer is before doing the study? Just do the study. Your hypothesis is nobody's business.

"Draw your assumptions before your conclusions" 5 years ago we launched our *free* Causal Diagrams course via HarvardX and edX. Since then, 80,000 people in 180 countries have registered. Check it out if you want to learn about DAGs and SWIGs for causal inference: www.edx.org/learn/data-a...