American Statistician, 68(1):8-13, February 2014. TECHNICAL REPORT R-414 Comment: Understanding Simpson’s Paradox Judea PEARL
Posts Tagged ‘why0mg’
r414-reprint.pdf – Simpson’s paradox
August 9, 2025Causal Analysis in Theory and Practice » Lord’s Paradox: The Power of Causal Thinking
August 9, 2025Goodreads & Amazon reviews
August 6, 2025Some “strong quotes” from the Book of Why
August 3, 2025QT:{{‘
Even two decades ago, asking a statistician a question like “Was it the aspirin that stopped my headache?” would have been like asking if he believed in voodoo. To quote an esteemed colleague of mine, it would be “more of a cocktail conversation topic than a scientific inquiry.” But today, epidemiologists, social scientists, computer scientists, and at least some enlightened economists and statisticians pose such questions routinely and answer them with mathematical precision.
“}}
QT:{{”
Ironically, the need for a theory of causation began to surface at the same time that statistics came into being. In fact, modern statistics hatched from the causal questions that Galton and Pearson asked about heredity and their ingenious attempts to answer them using
cross-generational data. Unfortunately, they failed in this endeavor, and rather than pause to ask why, they declared those questions off …
This was a critical moment in the history of science. The opportunity to equip causal questions with a language of their own came very close to being realized but was squandered. In the following years, these questions were declared unscientific and went underground. Despite heroic efforts by the geneticist Sewall Wright (1889–1988), causal vocabulary was virtually prohibited for more than half a century. And when you prohibit speech, you prohibit thought and stifle principles, methods, and tools. Readers do not have to be scientists to witness this prohibition. In Statistics 101, every student learns to chant, “Correlation is not causation.” With good reason! The rooster’s crow is highly correlated with the sunrise; yet it does not cause the sunrise. Unfortunately, statistics has fetishized this commonsense observation.
…
The rest of statistics, including the many disciplines that looked to it for guidance, remained in the Prohibition era, falsely believing that the answers to all scientific questions reside in the data, “}}
The Book of Why: The New Science of Cause and Effect: Judea Pearl, Dana Mackenzie: 9780465097609: Amazon.com: Books
August 2, 2025iPhone Notebook export for The Book of Why: The New Science of Cause and Effect
August 2, 2025Your Notebook exported from The Book of Why: The New Science of Cause and Effect is
https://www.goodreads.com/notes/36393702-the-book-of-why/114528832-mark-gerstein?ref=rsp
A Unification of Mediator, Confounder, and Collider Effects – PMC
August 31, 2024https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967310/
MacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185–1193. https://doi.org/10.1007/s11121-021-01268-xMacKinnon, D. P., & Lamp, S. J. (2021). A unification of mediator, confounder, and collider effects. Prevention Science, 22(8), 1185–1193.
https://doi.org/10.1007/s11121-021-01268-x
QT:{{”
Third-variable effects are not distinguishable solely by statistical methods. Each third-variable effect can be fit to the same data, and if the relations between the variables are substantial, there will be evidence for each effect. In this sense, the confounder, mediator, and collider models are equivalent, providing an equal representation of the information contained in the data for three variables (Stelzl, 1986). Although mediation, confounding, and collision may equally explain the statistical associations among three variables, they describe different causal relations among those variables. Like much recent research on causal analysis, this paper highlights the centrality of the causal model underlying a research study and the important distinction between the causal model and the statistical model. The appropriate causal model is determined by prior empirical research and theory. The statistical analysis provides estimates for the proposed causal model.
“}}
Bonaparte (DVI)
August 11, 2024Sex Bias in Graduate Admissions: Data from Berkeley | Science
August 23, 2020http://science.sciencemag.org/content/187/4175/398
Sex Bias in Graduate Admissions: Data from Berkeley
P. J. Bickel1, E. A. Hammel1, J. W. O’Connell1
Science 07 Feb 1975:
Vol. 187, Issue 4175, pp. 398-404
DOI: 10.1126/science.187.4175.398
A type of Simpson’s paradox
Simpson’s paradox – Wikipedia
August 23, 2020https://en.wikipedia.org/wiki/Simpson‘s_paradox
linkstream2 microblog 