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Iván Díaz @idiaz.bsky.social · 10/09/2026

(1/22) Over the last few days I have been discussing the role of formal causal inference in RCTs, and in general the role of formalism and rigor in statistics in twitter with @f2harrell.bsky.social. Thread with an example for why both are fundamental for biomedical research:

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Arman Oganisian @stablemarkets.bsky.social · 01/03/2026

The critique of unmeasured confounding is often levied in a lazy/broad way. It is trivially true in any observational study. But if the critic can't think of a plausible such confounder and posit a reasonable direction/magnitude of its bias then they're not doing productive science.

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Herb Susmann @herbps10.bsky.social · 25/09/2025

New preprint out on a way to handle structural and practical violations of the overlap (also known as positivity) assumption in causal inference -- as long as the outcome is bounded, we derive simple partial identification bounds on the ATE. With @alecmcclean.bsky.social and @idiaz.bsky.social

Non-overlap Average Treatment Effect Bounds by Herbert P. Susmann, Alec McClean, and Iván Díaz

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Aleksander Molak @alxndrmlk.bsky.social · 12/09/2025

He did it before Double Machine Learning I met with professor Mark van der Laan because I think his work is pretty incredible and it sometimes feels like a secret that only a few people know about, especially in industry. 1/ #CausalSky #StatSky #CausalInference

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Herb Susmann @herbps10.bsky.social · 03/09/2025

I have a new paper out on a simple way to do causal inference with left-censored outcomes. This comes up with environmental data because measurements often have a lower limit of detection -- e.g. a chemical is undetectable below a certain level www.tandfonline.com/doi/full/10....

tandfonline.com

Non-parametric treatment effect bounds for left-censored outcomes: estimating the effect of herbicide use on 2,4-D exposure

Causal inference is concerned with defining and estimating the effect of a exposure on an outcome. For example, the Average Treatment Effect (ATE), a causal inference concept, is defined as the pop...

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Dan Malinsky @danielmalinsky.bsky.social · 02/09/2025

I wrote something about statistics under authoritarianism

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Iván Díaz @idiaz.bsky.social · 02/09/2025

Underlying this there is a valid and worrisome criticism of causal inference in practice, but most comments criticizing CI as a field miss the fact that “x methodology is being abused in practice” can be correctly said about almost anything.

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Iván Díaz @idiaz.bsky.social · 24/08/2025

This is why I prefer causal assumptions in terms of exogenous vars in structural causal models rather potential outcomes. Sure, the former is often mathematically stronger, but the latter is inscrutable by subject matter experts.

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Alec McClean @alecmcclean.bsky.social · 08/04/2025

Excited to present on Thursday @eurocim.bsky.social on new work with @idiaz.bsky.social on (smooth) trimming with longitudinal data! "Longitudinal trimming and smooth trimming with flip and S-flip interventions" Prelim draft: alecmcclean.github.io/files/LSTTEs...

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Miguel Hernan @miguelhernan.org · 03/02/2025

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 👇

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Carl T. Bergstrom @carlbergstrom.com · 26/01/2025

Colombia. With two o’s.

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Edward H. Kennedy @edwardhkennedy.bsky.social · 30/09/2024

From twitter: A short thread: It amazes me how many crucial ideas underlying now-popular semiparametrics (aka doubly robust parameter/functional estimation / TMLE / double/debiased/orthogonal ML etc etc) were first proposed many decades ago. I think this is widely under-appreciated!

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Wenbo Wu @wenbowu.bsky.social · 15/01/2025

📢📢The 4th Lifetime Data Science Conference will take place May 28–30, 2025, at New York Marriott at the Brooklyn Bridge in Brooklyn, NY, USA. This event will feature keynotes by Drs. Nicholas Jewell and Mei-Ling Lee, short courses, 60+ invited sessions, and a banquet on May 29. Register and join us!

community.amstat.org

ASA Community

The ASA Community is an online gateway for member collaboration and connection.

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Alejandro Schuler @aschuler.bsky.social · 10/01/2025

Happy to announce some new work with my student Kaitlyn Lee! arxiv.org/abs/2501.04871 If you're not in the know, Riesz regression is a general tool to estimate things like propensity weights without actually having to know that they are propensity weights in the first place.

arxiv.org

RieszBoost: Gradient Boosting for Riesz Regression

Answering causal questions often involves estimating linear functionals of conditional expectations, such as the average treatment effect or the effect of a longitudinal modified treatment policy. By ...

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Iván Díaz @idiaz.bsky.social · 23/12/2024

Totally agree with this, and would double down: description of causal mechanisms is the foundation of science. If we can’t describe causal mechanisms, no interventions can follow.

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Iván Díaz @idiaz.bsky.social · 16/12/2024

I think it is a mistake to call one-step type estimators “debiased”. They are generally biased in a traditional sense. The problem that one-step type estimators address isn’t just about bias but more importantly about controlling the statistical behavior of the error defined as estimate minus truth.

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Iván Díaz @idiaz.bsky.social · 15/12/2024

OK I’ll bite, what’s stata? The plural of statum?

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Iván Díaz @idiaz.bsky.social · 13/12/2024

Thank you Alec for leading this project, I learned a lot! This paper has a very useful study of what contrasts are feasible in situations with many treatments and positivity violations, including necessary assumptions and efficient one-step estimators. Check it out!

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Iván Díaz @idiaz.bsky.social · 06/12/2024

I see renewed discussion on #statsky about the interpretation of confidence intervals. I will leave here this quote from Larry Wasserman's All of Statistics, which I love. Controlling one's lifetime proportion of studies with an interval that does not contain the parameter is surely desirable!

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Edward H. Kennedy @edwardhkennedy.bsky.social · 22/11/2024

New paper! arxiv.org/pdf/2411.14285 Led by amazing postdoc Alex Levis: www.awlevis.com/about/ We show causal effects of new "soft" interventions are less sensitive to unmeasured confounding & study which effects are *least* sensitive to confounding -> makes new connections to optimal transport

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The EuroCIM @eurocim.bsky.social · 22/11/2024

The European Causal Inference Meeting 2025 is coming to Ghent! ✨ Share your work with experts across the globe – abstract submission for oral & poster presentations is now open! eurocim.org/abstracts.html

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Edward H. Kennedy @edwardhkennedy.bsky.social · 12/11/2024

CMU Stats & Data Science is hiring! www.cmu.edu/dietrich/sta... forms.stat.ufl.edu/statistics-j...

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Iván Díaz @idiaz.bsky.social · 23/09/2024

Our Division is hosting its inaugural yearly Biostatistics Symposium, and this year the topic is Causal Inference! We have an exciting lineup of speakers listed below. If you are in the NYC area, please join us! Link to register in the QR below.

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