Home Statistics and Visualization Statistical Modeling, Causal Inference, and Social Science

Input feed: Statistical Modeling, Causal Inference, and Social Science

Sort by: Date tagged Date published

1
2
3
…
259

1 - 25 of 6453

Manned Mars Mission Miscellanea

https://statmodeling.stat.columbia.edu/2026/08/05/manned-mars-mission-miscellanea/

Statistical Modeling, Causal Inference, and Social Science 08/05/2026

miscellaneous
Survey Statistics: structured MRP to smooth survey weights

https://statmodeling.stat.columbia.edu/2026/08/04/survey-statistics-structured-mrp-to-smooth-survey-weights/

Statistical Modeling, Causal Inference, and Social Science 08/04/2026

bayesian
Walnutpie version 0.0.1 Released

https://statmodeling.stat.columbia.edu/2026/08/04/walnutpie-version-0-0-1-released/

Statistical Modeling, Causal Inference, and Social Science 08/04/2026

computing
“In that era, undergrad males at Madison and elsewhere, had to take ROTC classes, and he kept intentionally failing them because he was very leftwing politically.”

https://statmodeling.stat.columbia.edu/2026/08/04/in-that-era-undergrad-males-at-madison-and-elsewhere-had-to-take-rotc-classes-and-he-kept-intentionally-failing-them-because-he-was-very-leftwing-politically/

Statistical Modeling, Causal Inference, and Social Science 08/04/2026

art
Stand-up comedy—like teaching, and book writing—requires “a collaboration with the audience.”

https://statmodeling.stat.columbia.edu/2026/08/04/53426/

Statistical Modeling, Causal Inference, and Social Science 08/04/2026

literature
“Placebo tests deserve a model, not just a glance.”

https://statmodeling.stat.columbia.edu/2026/08/03/placebo-tests-deserve-a-model-not-just-a-glance/

Statistical Modeling, Causal Inference, and Social Science 08/03/2026

bayesian
What gets you is not what you don’t know but what you don’t know you don’t know.

https://statmodeling.stat.columbia.edu/2026/08/02/what-gets-you-is-not-what-you-dont-know-but-what-you-dont-know-you-dont-know/

Statistical Modeling, Causal Inference, and Social Science 08/02/2026

causal
Why quantitative understanding of effect sizes matters, even if all you care about is the presence of the effect

https://statmodeling.stat.columbia.edu/2026/08/01/how-can-we-train-researchers-and-consumers-of-research-to-put-numbers-in-perspective-as-a-matter-of-course/

Statistical Modeling, Causal Inference, and Social Science 08/01/2026

causal
What do we learn from bestseller regressions?

https://statmodeling.stat.columbia.edu/2026/07/31/what-do-we-learn-from-bestseller-regressions/

Statistical Modeling, Causal Inference, and Social Science 07/31/2026

economics
Posterior predictive checking is for non-Bayesians too!

https://statmodeling.stat.columbia.edu/2026/07/30/posterior-predictive-checking-is-for-non-bayesians-too/

Statistical Modeling, Causal Inference, and Social Science 07/30/2026

bayesian
“Over-coverage caught by pre-registration: 47 of 56 inside a stated 50% interval”

https://statmodeling.stat.columbia.edu/2026/07/29/over-coverage-caught-by-pre-registration-47-of-56-inside-a-stated-50-interval/

Statistical Modeling, Causal Inference, and Social Science 07/29/2026

bayesian
Eleven Kinds of Loneliness: Richard Yates and the tragedy of agency

https://statmodeling.stat.columbia.edu/2026/07/29/eleven-kinds-of-loneliness-richard-yates-and-the-tragedy-of-agency/

Statistical Modeling, Causal Inference, and Social Science 07/29/2026

literature
Survey Statistics: equivalent models, equivalent weights (locally)

https://statmodeling.stat.columbia.edu/2026/07/28/survey-statistics-equivalent-models-equivalent-weights-locally/

Statistical Modeling, Causal Inference, and Social Science 07/28/2026

causal
Selection effects can go both ways (for taxi drivers as well as the rest of us)

https://statmodeling.stat.columbia.edu/2026/07/28/selection-effects-can-go-both-ways/

Statistical Modeling, Causal Inference, and Social Science 07/28/2026

analysis
He fit the same statistical models with three different software and got much different estimates. It’s another dimension of the multiverse.

https://statmodeling.stat.columbia.edu/2026/07/27/53408/

Statistical Modeling, Causal Inference, and Social Science 07/27/2026

computing
People sometimes talk about “the Jewish vote,” but what’s relevant is not really the Jewish vote or Jewish public opinion; it’s really about campaign contributions and the news media. Also similar with Mormons.

https://statmodeling.stat.columbia.edu/2026/07/26/the-jewish-vote-is-not-about-the-jewish-vote-jewish-public-opinion-is-not-about-jewish-public-opinion/

Statistical Modeling, Causal Inference, and Social Science 07/26/2026

political
I don’t see journal review as a gatekeeping process that will keep erroneous articles from being published

https://statmodeling.stat.columbia.edu/2026/07/25/i-dont-see-journal-review-as-a-gatekeeping-process-that-will-keep-erroneous-articles-from-being-published/

Statistical Modeling, Causal Inference, and Social Science 07/25/2026

miscellaneous
How generic language shapes the development of social thought

https://statmodeling.stat.columbia.edu/2026/07/24/how-generic-language-shapes-the-development-of-social-thought/

Statistical Modeling, Causal Inference, and Social Science 07/24/2026

miscellaneous
Pretty maps of the NY mayoral election vote. (The meta-point here is that people have a (false) intuition that any complicated piece of information can be conveyed in a single plot.)

https://statmodeling.stat.columbia.edu/2026/07/23/pretty-maps-of-the-ny-mayoral-election-vote-the-meta-point-here-is-that-people-have-a-false-intuition-that-any-complicated-piece-of-information-can-be-conveyed-in-a-single-plot/

Statistical Modeling, Causal Inference, and Social Science 07/23/2026

graphics
He “washed his hands in a can of tetraethyl lead at a press conference, claiming he was ‘not taking any chance whatever’. He knew this to be a lie, having already succumbed to a bout of lead poisoning.”

https://statmodeling.stat.columbia.edu/2026/07/22/he-washed-his-hands-in-a-can-of-tetraethyl-lead-at-a-press-conference-claiming-he-was-not-taking-any-chance-whatever-he-knew-this-to-be-a-lie-having-already-succumbed-to-a-bout/

Statistical Modeling, Causal Inference, and Social Science 07/22/2026

miscellaneous
Survey Statistics: poststratification without population level information

https://statmodeling.stat.columbia.edu/2026/07/21/survey-statistics-poststratification-without-population-level-information/

Statistical Modeling, Causal Inference, and Social Science 07/21/2026

miscellaneous
Was this USDA survey really “redundant, costly, politicized, and extraneous”?

https://statmodeling.stat.columbia.edu/2026/07/21/was-this-usda-survey-really-redundant-costly-politicized-and-extraneous/

Statistical Modeling, Causal Inference, and Social Science 07/21/2026

health
Rebecca Makkai points out: Fancy three-dimensional sets are easier to construct in books than in movies, but harder to explain

https://statmodeling.stat.columbia.edu/2026/07/20/fancy-three-dimensional-sets-are-easier-to-construct-in-books-than-in-movies-but-harder-to-explain/

Statistical Modeling, Causal Inference, and Social Science 07/20/2026

economics
Ted-talking University of California professor asks sex trafficker for $3,000,000 because he thinks there’s a “50% chance” he’ll make “important discoveries” in telepathy

https://statmodeling.stat.columbia.edu/2026/07/19/university-of-california-professor-and-ted-talk-performer-asks-sex-trafficker-for-3000000-because-he-thinks-theres-a-50-chance-hell-make-important-discoveries-in-telepathy/

Statistical Modeling, Causal Inference, and Social Science 07/19/2026

miscellaneous
It’s all about the Super Pacs: How the New York Times completely misreported campaign contributions in the Maine Senate race

https://statmodeling.stat.columbia.edu/2026/07/18/its-all-about-the-super-pacs-how-the-new-york-times-completely-misreported-campaign-contributions-in-the-maine-senate-race/

Statistical Modeling, Causal Inference, and Social Science 07/18/2026

political

1
2
3
…
259

1 - 25 of 6453