Iván Díaz
@idiaz.bsky.social
Statistician. Associate prof. at NYU Grossman Department of Population Health. Causal inference, machine learning, and semiparametric estimation.
https://idiazst.github.io/website/
https://idiazst.github.io/website/
Reposted by Iván Díaz
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
September 25, 2025 at 5:23 PM
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
Reposted by Iván Díaz
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
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
September 12, 2025 at 8:29 AM
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
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
Reposted by Iván Díaz
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....
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
September 3, 2025 at 3:07 PM
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....
www.tandfonline.com/doi/full/10....
Reposted by Iván Díaz
I wrote something about statistics under authoritarianism
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
September 2, 2025 at 12:46 PM
I wrote something about statistics under authoritarianism
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.
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.
September 1, 2025 at 11:22 PM
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.
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.
💡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
August 24, 2025 at 2:38 PM
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.
Reposted by Iván Díaz
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...
"Longitudinal trimming and smooth trimming with flip and S-flip interventions"
Prelim draft: alecmcclean.github.io/files/LSTTEs...
April 8, 2025 at 3:34 PM
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...
"Longitudinal trimming and smooth trimming with flip and S-flip interventions"
Prelim draft: alecmcclean.github.io/files/LSTTEs...
Reposted by Iván Díaz
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 👇
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 👇
February 3, 2025 at 2:51 PM
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 👇
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 👇
Reposted by Iván Díaz
Colombia. With two o’s.
January 26, 2025 at 8:29 PM
Colombia. With two o’s.
Reposted by Iván Díaz
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!
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!
September 30, 2024 at 3:11 AM
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!
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!
Reposted by Iván Díaz
📢📢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!
ASA Community
The ASA Community is an online gateway for member collaboration and connection.
community.amstat.org
January 15, 2025 at 2:37 PM
📢📢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!
Reposted by Iván Díaz
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/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.
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 ...
arxiv.org
January 10, 2025 at 10:55 PM
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/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.
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.
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...
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...
Three Years of the Coronavirus Disease 2019 Pandemic in a European Region: A Population-Based Longitudinal Assessment in Madrid Between 2020 and 2022
Using population-based data from several linked administrative and clinical databases, we characterized the health burden of COVID-19 longitudinally in the near
academic.oup.com
December 23, 2024 at 10:46 AM
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.
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.
Isn't the whole point of the causal machine learning literature that these plug-in estimates are biased?
For instance Section 4.1 of arxiv.org/abs/2203.06469
For instance Section 4.1 of arxiv.org/abs/2203.06469
Semiparametric doubly robust targeted double machine learning: a review
In this review we cover the basics of efficient nonparametric parameter estimation (also called functional estimation), with a focus on parameters that arise in causal inference problems. We review bo...
arxiv.org
December 16, 2024 at 10:54 AM
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.
OK I’ll bite, what’s stata? The plural of statum?
December 15, 2024 at 8:38 PM
OK I’ll bite, what’s stata? The plural of statum?
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!
New-ish paper alert! arxiv.org/abs/2410.13522
We tackle the challenge of comparing multiple treatments when some subjects have zero prob. of receiving certain treatments. Eg, provider profiling: comparing hospitals (the “treatments”) for patient outcomes. Positivity violations are everywhere.
We tackle the challenge of comparing multiple treatments when some subjects have zero prob. of receiving certain treatments. Eg, provider profiling: comparing hospitals (the “treatments”) for patient outcomes. Positivity violations are everywhere.
Fair comparisons of causal parameters with many treatments and positivity violations
Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, with each targeting a different ...
arxiv.org
December 13, 2024 at 11:53 PM
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!
@wenbowu.bsky.social and I are looking for a postdoc! please reach out if you are interested.
Statistics Jobs - Statistics Forms
This is a current listing of job announcements related to Statistics. To submit a job for posting please use the Statistics Job Submission Form. Please note that jobs are posted within 24hrs of submis...
forms.stat.ufl.edu
December 13, 2024 at 5:40 PM
@wenbowu.bsky.social and I are looking for a postdoc! please reach out if you are interested.
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!
December 6, 2024 at 2:44 PM
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!
Reposted by Iván Díaz
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
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
November 22, 2024 at 4:39 AM
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
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
Reposted by Iván Díaz
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
November 22, 2024 at 3:45 PM
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
Reposted by Iván Díaz
November 12, 2024 at 3:10 AM
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.
September 23, 2024 at 2:28 PM
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.