I should add that I am looking to give talks on this work. If you're interested (or want to know about this work but don't have time to read the paper), please reach out I also might be able to pay my own way through some other sources, so you only need to invite me

This is one of my more out there papers. So, it is just sitting on arXiv (not submitted yet) because I am hoping to get additional feedback first So, please consider giving it a read and letting me know your thoughts arxiv.org/abs/2511.01960

I may have finally gone off the methodological deep end. A new preprint from me about some methodological ideas that have been brewing for years I'll give a bit more background on how I got here relative to the intro, and a brief overview of the paper arxiv.org/abs/2511.01960

I may have finally gone off the methodological deep end. A new preprint from me about some methodological ideas that have been brewing for years I'll give a bit more background on how I got here relative to the intro, and a brief overview of the paper arxiv.org/abs/2511.01960

I've had this on my reading list for awhile, but I finally got around to it and it is excellent It is a great introduction to bounds for missing data (that I wish I read much earlier when I was first learning about them!!) www.sciencedirect.com/science/arti...

We (Audrey Renson and @pausalz.bsky.social) Just updated this paper on interference in time-varying DiD settings from a while ago: arxiv.org/abs/2405.11781. Still see papers coming out regularly about problems that I'm pretty sure this solves...

BetterBibTex's automatic sync feature in Zotero is such a great feature ... if it would work... everytime I update in Zotero, BetterBib deletes all the year and journal fields for all the entries (unless I manually re-export, thus defeating the purpose)

If you've ever found any of my work helpful, consider donating to the Python Software Foundation Learning Python during my PhD and translating everything between programming languages helped me build my understanding of causal inference. It is also why I know estimating equations as well as I do

If you've ever found any of my work helpful, consider donating to the Python Software Foundation Learning Python during my PhD and translating everything between programming languages helped me build my understanding of causal inference. It is also why I know estimating equations as well as I do

Probably a good sign that all the Meta Ray-Ban AI glasses commercials just focus on their aesthetics or celebrities wearing them, and not what you could use them for The sign of a great product!!

To close my catch-up of #MEstimatorMonday (41/52), let's talk about how I use estimating equations with synthesis models

Give it up for day 4 of my #MEstimatorMonday catch-up week (40/52). Let's go over Bespoke IVs Like proximal causal inference, this is another way to address unmeasured confounding. For this, let's go over per-protocol effects in trials

For the 3rd day of my #MEstimatorMonday catch-up week (39/52), let's talk proximal causal inference We can think about proximal causal inference as an extension of the standard identification assumptions to allow for more rich data structures. Specifically, we can account for unmeasured confounding

For the 2nd day of the #MEstimatorMonday week (38/52), I'm going to workshop a notion I've been thinking about regarding the idea of stacking estimating functions

I have been bad about keeping up with #MEstimatorMonday so this will be a week of M-estimator (so 37 to 41/52) To start, let's talk computational aspects. As you might recall from the weeks on the sandwich variance, we need to compute derivatives. One option is automatic differentiation (autodiff)

That trick? Running different regression models on the same data until you get a small P-value for that super nutrient

For years I had trouble following some of the discussion about confidence bands, but at ACIC this year @noahgreifer.bsky.social pointed me to a helpful paper So you don't have to be as perplexed as I once was, we have a new pre-print introducing the key ideas arxiv.org/abs/2510.07076

For years I had trouble following some of the discussion about confidence bands, but at ACIC this year @noahgreifer.bsky.social pointed me to a helpful paper So you don't have to be as perplexed as I once was, we have a new pre-print introducing the key ideas arxiv.org/abs/2510.07076

Aptos is such a bad font, let along one to set as the default...

Can large language models stand in for human participants? Many social scientists seem to think so, and are already using "silicon samples" in research. One problem: depending on the analytic decisions made, you can basically get these samples to show any effect you want. THREAD 🧵

I'm going to do a quick #MEstimatorMonday (36/52) to show how the pooled logit EE can be used to construct inverse probability of censoring weights (IPCW) This is based on the algorithm in my pre-print, but needs a few tweaks arxiv.org/abs/2504.13291

A well-spent $10m per year on Bill Belichick. Glad our university leadership really knows where to invest wisely

Something a bit different for #MEstimatorMonday (35/52) I'm going to take advantage of microblogging as a format and live code a new estimating equation

I hope you're wearing your hardhat because we are building bridges this #MEstimatorMonday (34/52) More specifically, we are going to bridge different trials together to address a single question pubmed.ncbi.nlm.nih.gov/38110289/

I often find myself saying "what a time to be alive" but friends, today, defector.com/it-took-many...

It's the first day of classes here for the semester, so for today's #MEstimatorMonday (33/52), let's do some math So far we've seen how the M-estimation framework is useful computationally, so let's step back and see how it is also useful for statistical proofs

I can't remember the paper (or if it exists), so if anyone can help me out I thought there was a paper where an author suggested that all large database (eg NHANES) should require multiple comparison corrections for all possible associations one could look at. What is that paper?

I can't remember the paper (or if it exists), so if anyone can help me out I thought there was a paper where an author suggested that all large database (eg NHANES) should require multiple comparison corrections for all possible associations one could look at. What is that paper?

This seems like a really good issue of AJE. It's added 5 papers to my to-read list academic.oup.com/aje/issue/19...

Yeah, I don't think we disagree at all. But I will still fight you in the streets.

It's Wonday (yes I know its late), so another happy #MEstimatorMonday (32/52) Let's talk about something M-estimation has a bit of difficulty with: the median

CPC Fellow Jamie Slaughter-Acey says cuts to federal research funding are part of a larger pattern of political interference in science that puts the health of all people at risk. www.theguardian.com/world/2025/a...

I'm going to need the Greeks to start inventing some new letters

I'm at #JSM2025 (say hi if you are too), so a shorter #MEstimatorMonday (31/52) As setup for later weeks, let's talk about data fusion and why M-estimators are a useful way to build fusion estimators. Today is based on the following paper pubmed.ncbi.nlm.nih.gov/35388406/

The setup and simulations I did here are really what helped me to understand the distinction between confidence *intervals* and confidence *bands*

Today for #MEstimatorMonday (30/52), I'm going to workshop something I have been trying to clarify my own thinking about: confidence bands So, if something isn't clear, I'm incorrect, or there is something missing; passive-aggressively quote the offending skeet

Post just up: Is multiple imputation making up information? tldr: no. Includes a cheeky simulation study to demonstrate the point. open.substack.com/pub/tpmorris...

In draf, I had written “randomized” and “randomization” but changed them all to “randomised” and “randomisation” just to spite @faustobustos.bsky.social, @solomonkurz.bsky.social and @pausalz.bsky.social, who didn’t know how to compromise. bsky.app/profile/timp...

On this #MEstimatorMonday (29/52) let's see how we can implement sensitivity analyses for estimating the mean with missing data. This discussion is based on the following paper pmc.ncbi.nlm.nih.gov/articles/PMC...

On this #MEstimatorMonday (29/52) let's see how we can implement sensitivity analyses for estimating the mean with missing data. This discussion is based on the following paper pmc.ncbi.nlm.nih.gov/articles/PMC...

this is one of the greatest videos I’ve ever seen

Started making some nocino with black walnuts (which will be ready in December)

🚨 New paper in Statistics in Medicine! We compare TMLE vs CV-TMLE in tricky simulations: ✅ Misspecification ✅ Limited overlap ✅ High dimensions ✅ Super Learner + cross-validation Who performs best? Read it: onlinelibrary.wiley.com/doi/10.1002/... #CausalInference #TMLE #CVTMLE

Found this little (maybe big for a moth though) friend

and hopefully you'll be grateful that I will be ignoring your appeal

This is the week that I get @malcolmbarrett.malco.io to quit social media because #MEstimatorMonday (27/52) is on g-estimation and IV

This is the week that I get @malcolmbarrett.malco.io to quit social media because #MEstimatorMonday (27/52) is on g-estimation and IV

Whenever I read "logician" it always makes me think "magician". I think logician would be a pretty funny homebrew D&D class Like you learn a new theorem every level up

Randomly obsessed with simultaneous (uniform) inference. Feel free to ask about it. A mini thread on this follows. 🧵 Recommended reading: #statssky #econsky #episky

This was a nice short biography of Kurt Gödel. I knew about his theorem previously but it was interesting to learn more about him