Mastodawn

Single-world intervention graphs (SWIGs) as distributions. Systematic way to derive identifying expressions for estimands. New front-door derivations extending more readily to complex settings.

Conceptually, simultaneously related to and distinct from Rubin's framework and Pearl's calculus.

https://doi.org/10.48550/arXiv.2605.17050

#SWIG #DAG #causal #identification #frontdoor #docalculus

Single World Intervention Graphs as Distributions: A Framework for Causal Identification

Causal inference seeks to estimate the effect of an intervention on an outcome using observed data, typically via Rubin's potential-outcome framework or Pearl's do-calculus. Following section 9 of Richardson and Robins (2013), this essay treats single-world intervention graphs (SWIGs) as representations of both the observed-data distribution and the interventional distribution, rather than as a bridge to potential outcomes. We demonstrate that this perspective provides a systematic way to derive identifying expressions for estimands defined by interventions on selected variables. Back-door derivations mirror those in existing literature, while front-door derivations offer a distinct pathway that extends more readily to complex settings. Conceptually, the method is simultaneously related to and distinct from Rubin's framework and Pearl's calculus.

arXiv.org

Chris. Bart.Feb 22

The estimand framework, formalized in ICH E9(R1) regulatory guidance, provides a structured approach to define scientific objectives with precision. We apply the estimand framework to dose–exposure–response analyses. ... strategy to improve exposure–response analyses for dose selection, particularly when the relevant evidence includes data from multiple studies.

#estimand #exposure-response #dose-response #causal #pharmacometrics #pmx

https://doi.org/10.1002/psp4.70202

Dr. LabRat Feb 18

The vast datasets on which LLMs are trained are repositories of human linguistic activity, imbued with the collective referential histories of countless speakers. Even if an #LLM does not "know" these histories in a human-like conscious sense, the statistical patterns it learns from this data implicitly encode these #causal-historical links.

https://jordivitria.substack.com/p/meaningful-language-understanding

Meaningful Language Understanding in LLMs

Do LLMs understand language, or do they only process symbols statistically?

Jordi’s Substack

Dr Mircea Zloteanu 🌺🌞🍃Feb 10

#statstab #482 Introducing Causion: A web app for playing with DAGs

Thoughts: A very cool app. Let's you see exactly what your assumptions and DGP mean for your causal model.

#causal #causalinference #DAG #DAGs #dgp #tutorial #guide #education #pedagogy

https://pedermisager.org/blog/causion-dag-simulator/

Introducing Causion: A web app for playing with DAGs | Peder M. Isager

Personal website of Dr. Peder M. Isager

Peder M. Isager

Dr Mircea Zloteanu 🌺🌞🍃Feb 2

#statstab #476 Experimental : causal

Thoughts: Randomized experiments are the gold standard for inference for a reason. But they are hard to design.

#design #r #statistics #methods #experiment #tutorial #pedagogy #education #hypothesis #nhst #causal #ancova

https://book.declaredesign.org/library/experimental-causal.html

18 Experimental : causal – Research Design in the Social Sciences

DSLC Videos Jan 22

From the @DSLC chives:

ISLR: Deep Learning https://youtu.be/1D6plTaDvTU #RStats

The Effect: An Introduction to Research Design and Causality: Finding Front Doors https://youtu.be/8RJxoOz2dyg #RStats #causal #causality

Geocomputation w R: Statistical learning & Ecology https://youtu.be/ozXzmWtv1_g #geocomputation #RStats

Support the Data Science Learning Community at https://patreon.com/DSLC

ISLR: Deep Learning (islr01 10)

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DSLC Videos Jan 12

From the @DSLC chives:

Bayes Rules! Posterior Inference & Prediction https://youtu.be/5U19BRPwPrs #RStats

Modelado Tidy con R - 5. Gastando nuestros datos https://youtu.be/_E_5pBFtSJk #RStats

The Effect: Treatment Effects & Causality with Less Modeling https://youtu.be/G3Ur3lZAYCs #RStats #causal #causality

Support the Data Science Learning Community at https://patreon.com/DSLC

Bayes Rules! Posterior Inference & Prediction (bayes_rules04 8)

Federica Gazzelloni presents Chapter 8 ("Posterior Inference & Prediction") from Bayes Rules! by Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu on 2023-04-...

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