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Malcolm 朝精 Barrett
@malcolmbarrett.malco.io
Ph.D., epidemiology. research software engineer @ Stanford Health Policy. open-source data science. causal inference. information-shaped sentences. doing poems on aircrafts. approximately Bayesian. formerly Posit, Apple. 心を燃やせ。
570 followers245 following202 posts
MBmalcolmbarrett.malco.io

We just overhauled the first chapter of our book, Causal Inference in R! Learn about causal questions, their structure, and the relationship between description, prediction, and causal inference. www.r-causal.org/chapters/cha...@lucystats.bsky.social@travisgerke.bsky.social #rstats

A screenshot of the first chapter of Causal Inference in R: What is a causal question?
A sentence diagram of a causal question showing it's essential components: exposure, outcome, eligibility criteria, time-zero, target population, and follow-up period
A visualisation of a descriptive question: what were the number of deaths in 2020 vs the expected deaths based on historical data (by country)? The figure shows the shocking toll of the early pandemic, with actual 2020 deaths far above expected deaths. This is a useful graph for understanding risk without making causal claims
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TFtfeend.bsky.social

Kinda looks like something I should add to my page of resources tbh.

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JMdingdingpeng.the100.ci

That looks awesome 😮!

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MB
Malcolm 朝精 Barrett
@malcolmbarrett.malco.io
Ph.D., epidemiology. research software engineer @ Stanford Health Policy. open-source data science. causal inference. information-shaped sentences. doing poems on aircrafts. approximately Bayesian. formerly Posit, Apple. 心を燃やせ。
570 followers245 following202 posts
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