Iván Díaz
@ildiazm
Statistician. Associate prof. at NYU Grossman Department of Population Health. Causal inference, machine learning, and semiparametric estimation.
New paper and software alert! arxiv.org/abs/2408.14620 Interested in modern mediation analysis methods with machine learning and multivariate mediators? Take a look at this joint work with Richard Liu, @nickWillyamz , and @kara_rudolph Short 🧵...
Excited to share that our paper "Self-Calibrating Conformal Prediction" with @_ahmedmalaa is accepted at #NeurIPS2024! 🚀 We combine model calibration and prediction intervals by integrating Venn-Abers into conformal prediction. #conformal #calibration arxiv.org/abs/2402.07307
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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.
Interested in data-driven covariate adjustment? I’m presenting some recent work with @ildiazm and @SVansteelandt next week!
Our next journal club is scheduled on Sep 13 at 11am EST. Speaker: Kelly Van Lancker, Ghent University Zoom link: umich.zoom.us/j/7573650566 Title: Automated, efficient and model-free inference for randomized clinical trials via data-driven covariate adjustment
A tutorial on Longitudinal Modified Treatment Policies-- a flexible method for defining, identifying, and estimating causal parameters of interest-- is now in @EpidemiologyLWW! 🔗journals.lww.com/epidem/abstrac… cc🌟coauthors: @dasalazarb @nickWillyamz @kara_rudolph @ildiazm
updated tutorial on Longitudinal Modified Treatment Policies is now on arxiv! 🔗 arxiv.org/abs/2304.09460… for those at ACIC, i'll be hanging out by this poster today from 5-6:30pm and would love to chat about LMTPs, methodology tutorials, etc.
arXiv -> alphaXiv Students at Stanford have built alphaXiv, an open discussion forum for arXiv papers. @askalphaxiv You can post questions and comments directly on top of any arXiv paper by changing arXiv to alphaXiv in any URL!
Sorry to miss #SER2024 but we ♥️ the lmtp package and can’t wait to see what @nickWillyamz @kara_rudolph and @ildiazm have in the pipeline. Thank you very much & also to @kat_hoffman_ for the great tutorials!
If you are coming to #SER2024 and are interested in learning how to define and estimate causal effects for complex exposures (continuous, multivariate, ordinal, etc) in longitudinal studies using off-the-shelf software, join us in this workshop!
If you are coming to #SER2024 and are interested in learning how to define and estimate causal effects for complex exposures (continuous, multivariate, ordinal, etc) in longitudinal studies using off-the-shelf software, join us in this workshop!
@societyforepi pals! @ildiazm @nickWillyamz &I are leading a workshop Tues pm at #SER2024 on estimating causal effects of multiple or nonbinary exposures. @nickWillyamz is an absolute wizard and made a beautiful, user-friendly workshop with lots of examples in R. Register & join!
I have been for a long time trying to understand Frank’s views but every day I am more baffled. If there is rarely any treatment effect heterogeneity, why the insistence on conditional treatment effects? Shouldn’t they be equal to marginal in that case?
Just from reading the abstract, there is nothing to budge my belief in the rarity of ACTIONABLE heterogeneity of treatment effect. Sure you can mimic data with models that allow for HTE but identifying beforehand pts likely to have large benefit is another thing.
What are the differences between one-step estimation, Double ML, and Targeted ML? This commentary (@ildiazm) and blog post (@mark_vdlaan) provide an overview of the history of machine learning in semiparametrics. pubmed.ncbi.nlm.nih.gov/31742333/ vanderlaan-lab.org/2019/12/24/cv-…