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Iván Díaz

@idiaz.bsky.social

1.2K followers 230 following 57 posts

Statistician. Associate prof. at NYU Grossman Department of Population Health. Causal inference, machine learning, and semiparametric estimation. https://idiazst.github.io/website/

idiazst.github.io

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Reposted by Iván Díaz

Herb Susmann @herbps10.bsky.social · 15d

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

Reposted by Iván Díaz

Aleksander Molak @alxndrmlk.bsky.social · 29d

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.

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#CausalSky #StatSky #CausalInference

Iván Díaz @idiaz.bsky.social · Sep 11

But I do agree that the addendum is progress from the previous state of things!

Iván Díaz @idiaz.bsky.social · Sep 11

But more seriously, the addition would have been a sentence and a couple of references, not a complete framework, which already exists.

Iván Díaz @idiaz.bsky.social · Sep 11

The better analogy is a bus that drops you off midway to your destination, not even in a bus stop, and steals your phone so you can’t get home 😉

Iván Díaz @idiaz.bsky.social · Sep 11

I think the addendum fell short and should have described a need and approaches to mathematically define and identify estimands.

Iván Díaz @idiaz.bsky.social · Sep 10

Thanks for sharing. Tangential to the thread but I have to say that I disagree with the statement of the abstract that causal inference estimands correspond to an effect in an ideal trial. Many valid scientific questions are expressed in terms of parameters that cannot be identified in trials!

Iván Díaz @idiaz.bsky.social · Sep 10

What I do not follow about both TTE and addendum is why reinvent the wheel. I get it if it is about communicating to new audiences, but not if they are presented as new methods when they are so clearly inferior to what's already out there.
bsky.app/profile/idia...

Iván Díaz @idiaz.bsky.social · Sep 10

TTE is certainly useful (I use it). But it is not a replacement for formal causal models + causal estimands + identification + optimal estimation etc. I recommend reading Maya Petersen and colleagues' papers on the "roadmap for causal inference."

Iván Díaz @idiaz.bsky.social · Sep 10

TTE is certainly useful (I use it). But it is not a replacement for formal causal models + causal estimands + identification + optimal estimation etc. I recommend reading Maya Petersen and colleagues' papers on the "roadmap for causal inference."

Iván Díaz @idiaz.bsky.social · Sep 10

Presenting TTEs as a method rather than as a communication tool had the unintended consequence of folks slapping the moniker in studies as a quality signifier without doing the actual leg work required to address the issues, discussing them, or even understanding them.

Iván Díaz @idiaz.bsky.social · Sep 10

Right, IMO target trials are a great *communication tool* to talk with folks who do not have in-depth causal inference training (hence their success), but to really understand the issues one has to rely on standard theory (causal models, identification, estimands, optimal estimation, etc.)

Iván Díaz @idiaz.bsky.social · Sep 10

Curious to hear if the gripes are substantive or if they are attribution-type (e.g., causal inference was using estimands long before the addendum).

Reposted by Iván Díaz

Herb Susmann @herbps10.bsky.social · Sep 3

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....

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...

www.tandfonline.com

Reposted by Iván Díaz

Dan Malinsky @danielmalinsky.bsky.social · Sep 2

I wrote something about statistics under authoritarianism

The Guardian @theguardian.com · Sep 2

Why Trump’s undermining of US statistics is so dangerous | Daniel Malinsky

Why Trump’s undermining of US statistics is so dangerous | Daniel Malinsky

From the firing of the labor statistics chief to plans for a new census, the president’s moves serve to entrench authoritarianism

www.theguardian.com

Iván Díaz @idiaz.bsky.social · Sep 2

It should be telling that it is the field of CI that has given folks the tools to understand the conditions under which observational studies deliver causal effects. You may argue whether those conditions are ever achievable, but criticizing CI for achieving its goal seems silly.

Iván Díaz @idiaz.bsky.social · Sep 1

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.

Darren Dahly @statsepi.bsky.social · Aug 31

We didn't randomize, and there was no allocation concealment or blinding, and we can't really be sure what intervention they got or how the outcomes were measured, but we emulated a trial by drawing a DAG.

Iván Díaz @idiaz.bsky.social · Aug 29

Clinician: How do I make sure Y(a) is independent of A conditional on covariates

Statistician: You measure all common causes of A and Y…

🤷🏻

Iván Díaz @idiaz.bsky.social · Aug 24

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.

Paul Hünermund @p-hunermund.com · Aug 24

💡A new paper by Elias Bareinboim and Drago Plecko underscores the intractability of ignorability assumptions commonly invoked in the potential outcomes framework, explains why structural causal models—explicitly grounded in well-defined causal mechanisms—are far easier to interpret. 1/2

Iván Díaz @idiaz.bsky.social · Aug 8

I guess I would agree that Borges was pretentious:

www.youtube.com/watch?v=NJYo...

J. L. Borges on English

YouTube video by Andrea Cirla

www.youtube.com

Iván Díaz @idiaz.bsky.social · Jul 31

I think missing data is a CI problem (counterfactual is "would have observed the data") but not the opposite. E.g., recasting mediation analyses etc as a missing data problems seems contrived.

Iván Díaz @idiaz.bsky.social · Jul 31

Cheap non alcoholic cava in a tetra pack container. How come the outcomes have to be “potential”?

Iván Díaz @idiaz.bsky.social · Jul 31

At some point we’ll have to give these guys some agency 😉

Iván Díaz @idiaz.bsky.social · Jul 31

Re: the OP, it also seems right to me that stats should focus on the properties of estimators, but the stubborn rejection of the language that links stats to science (CI) by some “trad” statisticians seems very odd to me

Iván Díaz @idiaz.bsky.social · Jul 31

On this, I agree with Pearl and others who have emphasized that estimators aren’t causal. A causal estimand equals a statistical estimand (usually) under assumptions; estimators target statistical estimando which may or may not have a causal interpretation.

Reposted by Iván Díaz

Alec McClean @alecmcclean.bsky.social · Apr 8

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...