Tom StaffordAlready have my 2026 resolution ready
Dr Mircea Zloteanu ❄️☃️🎄#statstab #471 Give Your Hypotheses Space!
Thoughts: "each hypothesis requires its own model" + "Only interpret the output for your exposure of interest"
#causalinference #modelling #hypothesis #tutorial #confounding #mbias
https://brian-lookabaugh.github.io/website-brianlookabaugh/blog/2025/mutual-adjustment/
#statstab #456 Shall we count the living or the dead?
Thoughts: survival ratio -> if the intervention increases risk of the outcome
risk ratio -> if the intervention reduces risk of the outcome
Shall we count the living or the dead?
In the 1958 paper "Shall we count the living or the dead?", Mindel C. Sheps proposed a principled solution to the familiar problem of asymmetry of the relative risk. We provide causal models to clarify the scope and limitations of Sheps' line of reasoning, and show that her preferred variant of the relative risk will be stable between patient groups under certain biologically interpretable conditions. Such stability is useful when findings from an intervention study must be generalized to support clinical decisions in patients whose risk profile differs from the participants in the study. We show that Sheps' approach is consistent with a substantial body of psychological and philosophical research on how human reasoners carry causal information from one context to another, and that it can be implemented in practice using van der Laan et al's Switch Relative Risk, or equivalently, using Baker and Jackson's Generalized Relative Risk Reduction (GRRR).
#statstab #446 {causaldata} Packages of Example Data for The Effect
Thoughts: On your journey to learning Causal Inference you can use some nice datasets to figure out how horrible it can all go.
“Correlation is causation” 😈
New #substack going over the maths of correlation, t-test, and linear models. https://open.substack.com/pub/mzloteanu/p/correlation-is-causation?r=3b457w&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
#statstab #440 Computing Statistical Power for the Difference in Differences Design
Thoughts: DiD studies are all the rage in Obs research. But how does the concept of power apply to them?
#poweranalysis #DiD #causalinference #samplesize #observational
New newsletter! Understanding participation in the X Community Notes system, via my love of data visualisation and stronger causal inference methods!
#DataViz #CausalInference #FactChecking #CommunityNotes
https://open.substack.com/pub/tomstafford/p/community-notes-require-a-community
#statstab #434 Exposing omitted moderators: Explaining why effect sizes differ in the social sciences
Thoughts: Maybe our models are too simple to makdle the generalisable claims we want.
Pierre-Simon Laplace🧪 Causal inference is about understanding why things happen, not just what
Alex Andorra talks with Sam Witty about ChiRho & how probabilistic programming is reshaping interventions, counterfactuals, and the future of causal reasoning
🎧https://learnbayesstats.com/episode/141-ai-assisted-causal-inference-sam-witty
#CausalInference #BayesianStatistics #Podcast #DataScience #AIResearch #LearningBayesianStatistics #NewEpisode
jobRxivPostdoc in Single-Cell Multi-Omic Gene Regulatory Networks
University of Massachusetts Chan Medical School
Decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference and #DiffEq as a #postdoc! No biomed bg needed.
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-massachusetts-chan-medical-...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks-2/?fsp_sid=2615
