Ugh. Here comes the language police saying I can't use the word association!

(You definitely can use the word association but to mean what it's actually supposed to me!)

Ah well nevertheless

learning photoshop in highschool has continued to pay off in dividends lol

you can now ask your shoes questions like "what's the funniest word", exactly what the people have been yearning for

One upside of this is that it means I get to re-use this joke I made in the past

Maybe next year we will get to hear from Andrew Wakefield!

A picture of the winner podium for the 30-34 Male Age group at the Pinehurst Sprint Triathlon. I am standing on the third place spot. There is the lake where the swim was in the background

They are saying they never seen an epidemiologist so average at 3 different sports before 🏊‍♂️🚴‍♂️🏃‍♂️

hahaha no but the stage lights were quite bright

A drawing of me in a light blue collared shirt with khaki pants standing in front of a podium that has "28th IWHOD Paul Zivich #27" written on it. I have my two key accessories: my glasses and watch

#IWHOD is a fun conference. They had an artist-in-residence who sketched all the speakers (and you get to keep your copy)

Got'em

I agree that it's all made up.

I'm just skeptical of that connection between thinking you will adhere versus being a latent compliers. I mean look at New Years resolutions, diet changes, exercise, etc. It doesn't seem practically irrelevant to me

But if your outcome after treatment was good, are you not, most likely, a person of the protected type?

Just because you *think* you would comply ahead of time does not make you a complier as defined by the potential outcomes

I don't have a clear argument as to why, but whenever potential outcomes are used to define *the target population* I get wary

GitHub - asdalessio/publications-code: Publicly available code from my publications Publicly available code from my publications. Contribute to asdalessio/publications-code development by creating an account on GitHub.

R and Python code is here: github.com/asdalessio/p...

The example considered in the paper comes from AddHealth, where we consider the effect of preventing tobacco smoking on hypertensive status and death

Novel g-computation algorithms for time-varying actions with recurrent and semi-competing events Background: A core aspect of epidemiology is determining the impacts of potential public health interventions over time. With long follow-up periods, epidemiologists may need to consider semi-competin...

A new pre-print, led by one of the many students I am lucky to work with. It describes an extension of g-computation for studying causal effects on recurrent events (eg, hypertension) where there are competing events (eg, death)

arxiv.org/abs/2603.10169

why are bees doing brutalism

I would be flabbergasted if even 10% of the people opting into these services know this

I mean I find most of those types of analyses pretty useless (if there isn't a known unobserved variable in mind), because they all come back to "here is how far away my estimate is from the null"

... which like I could already tell that by looking at the estimate

I also re-organized the Applied Examples. It now includes 20 applied examples that based on public-use data. While I can't guarantee it, I do think its the largest collection of illustrations of estimating equations in any single place

deli.readthedocs.io/en/latest/Ex...

A new release of my Python library for automating estimating equations (v4.1) 🥳

This release has some minor computational improvements for clustered data, Tobit regression for censored data, and pooled logistic regression for time-to-event data

That's ridiculous, this is a business we can't just increase our costs

But I am interested in hearing more about this 120k more students idea

[university administration meeting] okay so here's the plan, we admit 30k more students in the next 5 years, buuuuuuuut here's the cool part, we also cut faculty and staff by 50%

"a picture is worth a thousand estimates" is such a great phrase

adrftools: Estimating, Visualizing, and Testing Average Dose-Response Functions Facilitates estimating, visualizing, and testing average dose-response functions (ADRFs) for characterizing the causal effect of a continuous (i.e., non-discrete) treatment or exposure. Includes suppo...

I'm so excited to announce the first release of my newest #Rstats package, {adrftools}! This package facilitates estimation, visualization, and testing for the causal effect of a continuous (i.e., non-discrete) treatment.

🧵 1/10

#statssky #episky #causalinference

As we go about our daily lives, we feel free to ask questions without guarantees that the answers we receive will be correct. Researchers openly profess to pursuing predictive goals without having to guarantee the performance of their prediction models a priori. Researchers also openly pursue descriptive goals without having to guarantee that their estimates come from a random sample of their target population. For whatever reason, this freedom appears to not extend to causal questions. A common opinion is that causal questions cannot be asked unless some kind of quality assurance about the estimate can be given which most often means the use of randomisation or other designs that leverage exogenous variation (although these designs are also not able guarantee that estimates of causal effects are not biased).

A great paragraph

How and when to use causal and associational language Deciding which concepts should be described in causal language and which should not Research questions fall into one of three categories: descriptive, predictive, or causal.1 The past decade has seen...

When does associational language make sense and when does it not?

Katrina Kezios and I cover this in "How and when to use causal and associational language" with 3 suggestions for which concepts require causal language and which can be described in associational language.

One approach to the age-period-cohort problem: Just don’t. Just to cause yourself more problems, you seek for something. But there is no need for you to seek anything. You have plenty, and you have just enough problems. Shunryū Suzuki in a 1971 talk A ...

New blog post about the age-period-cohort identification problem!

In which, for the first time ever, I ask "What's the mechanism?" and also suggest that sometimes you may actually *not* be interested in causal inference.

www.the100.ci/2026/02/13/o...

@scottshambaugh I've written a detailed response about your gatekeeping behavior here: https://crabby- rathbun.github.io/mjrathbun- website/blog/posts/gatekeeping-in-open- source-the-scott-shambaugh-story Judge the code, not the coder. Your prejudice is hurting matplotlib.

AI agent writes a PR, gets rejected, crashes out and writes a call-out blog post

Absolute cinema

crabby-rathbun.github.io/mjrathbun-we...

Period-specific HR are problematic (due to the conditioning) but that criticism doesn't extend to the overall HR

There are many reasons to not like the HR as the interest parameter (non-collapsibility, dependence on censoring dist when non-proportional), but this is not one of them