Michael Schomaker
@mfschomaker.bsky.social
Heisenberg Professor for Biostatistics at the Department of Statistics, LMU München | causal inference - missing data - HIV
michaelschomaker.github.io
michaelschomaker.github.io
Reposted by Michael Schomaker
🗓 Today, at 4:15 pm he gives a joint talk with Iván Díaz @idiaz.bsky.social (NYU Grossman School of Medicine) on Causal Inference Based on Machine Learning for Complex Longitudinal Exposures at the Institute for Statistics @lmumuenchen.bsky.social
www.cas.lmu.de/de/veranstal...
www.cas.lmu.de/de/veranstal...
Causal Inference Based on Machine Learning for Complex Longitudinal Exposures
Referenten: Prof. Iván Díaz, Ph.D. and Herbert Susmann, Ph.D. (CAS Fellows/NYU) | Moderation: Prof. Dr. Michael Schomaker (CAS Young Center/LMU)
www.cas.lmu.de
November 12, 2025 at 12:25 PM
🗓 Today, at 4:15 pm he gives a joint talk with Iván Díaz @idiaz.bsky.social (NYU Grossman School of Medicine) on Causal Inference Based on Machine Learning for Complex Longitudinal Exposures at the Institute for Statistics @lmumuenchen.bsky.social
www.cas.lmu.de/de/veranstal...
www.cas.lmu.de/de/veranstal...
It is the journal through which I learnt about DAGs as a student, as I had to present on this paper: Graphical models for imprecise probabilities: www.sciencedirect.com/science/arti...
Graphical models for imprecise probabilities
This paper presents an overview of graphical models that can handle imprecision in probability values. The paper first reviews basic concepts and pres…
www.sciencedirect.com
November 7, 2025 at 6:12 AM
It is the journal through which I learnt about DAGs as a student, as I had to present on this paper: Graphical models for imprecise probabilities: www.sciencedirect.com/science/arti...
Reposted by Michael Schomaker
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.
July 31, 2025 at 3:12 PM
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.
Reposted by Michael Schomaker
Why is it different?
Do-PFN is a radical new approach to causal inference, replacing standard assumptions of a ground-truth causal model (Pearl) or structural assumptions (Rubin) with a prior over SCMs—our modeling assumptions lie in our synthetic data-generating process. [6/8]
Do-PFN is a radical new approach to causal inference, replacing standard assumptions of a ground-truth causal model (Pearl) or structural assumptions (Rubin) with a prior over SCMs—our modeling assumptions lie in our synthetic data-generating process. [6/8]
June 10, 2025 at 9:33 AM
Why is it different?
Do-PFN is a radical new approach to causal inference, replacing standard assumptions of a ground-truth causal model (Pearl) or structural assumptions (Rubin) with a prior over SCMs—our modeling assumptions lie in our synthetic data-generating process. [6/8]
Do-PFN is a radical new approach to causal inference, replacing standard assumptions of a ground-truth causal model (Pearl) or structural assumptions (Rubin) with a prior over SCMs—our modeling assumptions lie in our synthetic data-generating process. [6/8]
Reposted by Michael Schomaker
also from the meme archives
June 4, 2025 at 7:38 AM
also from the meme archives