ICYMI this paper really is a must read: https://doi.org/10.1111/ele.14033

Comparing and choosing best models based on AIC etc, then interpret coefficients causally (what's the effect of X on Y?) is flawed, yet so common

We must draw causal assumptions first (DAG)

"Model selection is not a valid method for inferring causal relationships. It's appropriate for predictive inference (which model best predicts Y?), which is fundamentally distinct from causal inference (what is the effect of X on Y?)"

1/

Andrew Gelman on variable selection: "Variable selection (that is, setting some coefficients to be exactly zero) can be useful for various reasons, including:
– It’s a simple form of regularization.
– It can reduce costs in future data collection.
Variable selection can be fine as a means to an end. Problems can arise if it’s taken too seriously, for example as an attempt to discover a purported parsimonious true model."

https://statmodeling.stat.columbia.edu/2023/07/18/when-your-regression-model-has-interactions-do-you-need-to-include-all-the-corresponding-main-effects/#comments

Hear, hear. Saving this for future use.

Musk revokes Twitter accounts for transit agencies, as they would compete against Tesla. Americans call this Free Markets.

https://octodon.social/@ethanz/109683937494252761
[email protected] - As Twitter suspends city services running on its infrastructure, maybe the question we should ask is "Why did we trust our services to privately owned platforms in the first place?" Excellent piece by @[email protected] of Bloomberg: https://www.bloomberg.com/news/articles/2023-01-13/despite-chaos-cities-are-sticking-with-twitter?sref=ndQeXqOs

Still getting the hang of Mastodon. Hope to use it more in the new Year for conferences and #foamed.

In the meantime- Happy New Year, friends. 🥂

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