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Reposted by Jeremy Labrecque 🇨🇦

Saloni @scientificdiscovery.dev · 8d

Most graphs of the fertility rate depict the 'period fertility rate', which is based on a single year's data and doesn't necessarily reflect how many children women actually have across their lifetimes.

I've used data from the Human Fertility Database to show the cumulative number instead:

Cohort fertility rates for the United States, by age 40, 45 and 50.

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Darren Dahly @statsepi.bsky.social · 8d

Science is grounded in observation. Measurement is a tool for observation. Measurements should be evaluated for validity and reliability/uncertainty. Scientists who use measurements without understanding their properties are not really scientists at all.

Jamie Cummins @jamiecummins.bsky.social · 21d

Can large language models stand in for human participants?
Many social scientists seem to think so, and are already using "silicon samples" in research.

One problem: depending on the analytic decisions made, you can basically get these samples to show any effect you want.

THREAD 🧵

The threat of analytic flexibility in using large language models to simulate human data: A call to attention

Social scientists are now using large language models to create "silicon samples" - synthetic datasets intended to stand in for human respondents, aimed at revolutionising human subjects research. How...

arxiv.org

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 8d

People worry about black-box models.

This is black-box data.

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Jake Grumbach @jakemgrumbach.bsky.social · 9d

Since the Ezra & Ta-Nehisi discussion is still happening: the main point I think most are missing is that Klein is saying the role of the journalist-intellectual is to do strategic politics, whereas Coates says the role of the journalist-intellectual is to tell the truth

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 9d

…about the plausibility of the causal assumptions which requires strong knowledge of causal inference and strong substantive knowledge.

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 9d

Yeah. One thing that I think is often missing when people push for causal language in the research question is that it in NO WAY makes your answer more causal. The only thing that gives a stronger causal interpretation to your estimate is convincing arguments…

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 9d

One reason I oppose associational or other non-causal language in research questions is it lets people think they don't need causal inference to answer causal questions.

(When the underlying question is causal, of course. Which it almost always is.)

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Dominique Baker @bakerdphd.bsky.social · 10d

I'm gonna be saying "was silence not an option?" from now on

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 10d

We argued with the editors but they failed to understand this basic idea. (And even if the student was interested in the causal effect, all the adjustment variables were mediators so you shouldn't adjust for them anyway). The student ended up taking the paper to another journal.

Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 10d

I knew a student who just wanted to report the unadjusted difference in some outcomes by sex. To know whether these were different in men and women. The editors insisted they needed to adjust for a whole host of other variables (as though the student was trying to answer a causal question).

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 10d

And, in my experience at least, epidemiologists have the opposite problem you describe. They don't over-interpret the unadjusted estimate. They assume that the only things worth knowing must be adjusted for every available covariate. Which is very wrong.

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 10d

Another reason might be that when your adjusted estimate is far from the real world, that suggests that you're relying on your model for inference which is a good thing to know, at least I like to know how worried I need to be about model specification.

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 10d

And the unadjusted estimate does tell you something. In the absence of selection bias it is the descriptive difference in the outcome by exposure group. Descriptive statistics tell you the state of the real world which is very useful and important to know. But, ok maybe you're not interested in that

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 10d

What you're saying is that you think you know the DGM with respect to age and therefore any estimate without adjusting for age is non-informative. I would say that you should never be so sure of your DGM. The data could have been sampled in a weird way. Or your population could be different.

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Olga Lautman 🇺🇦 @olgalautman.bsky.social · 10d

Congrats to Moldova!!! Russia threw everything to destabilize the election and failed spectacularly.

Democracy won 🙌🏼

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JazzReads📚 @jazzreads.bsky.social · 10d

So exhausting..

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 11d

A simple example, if I read a paper and adjustment for a variable I believe is a strong confounder results in no or little change in estimate (of a collapsible effect measure), that might make me wonder whether that variable is poorly measured, for example, or that there is some other problem.

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 11d

If the estimate changed (or didn't change) in an unexpected way and you can't explain it, that's a sign that you don't understand your data-generating mechanism really at all. And I would therefore put much less credence in your adjusted estimate.

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 11d

I've never really questioned reporting both so I'm willing to change my mind here.

But my way of thinking about this is: to convince me that your adjusted estimate is even close to the causal effect, you have to convince me that you understand the data-generating mechanism pretty well.

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Robert (Bob) Kubinec @rmkubinec.bsky.social · 11d

And to be fair, many academic journals also ban "causal" words like effect/impact/due to.

Conrad Hackett @conradhackett.bsky.social · 11d

How common are blasphemy laws? About 4-in-10 countries have them.
www.pewresearch.org/short-reads/...

Map showing 18 countries in the Middle East North Africa region had blasphemy laws in 2019.

Map showing most countries that had apostasy laws in 2019 were in the Middle East and North Africa region.

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Dr. Catherine Scott @cataranea.bsky.social · 13d

Canada Post “lost” 1 billion dollars last year?

How about, “it cost Canadians 1 billion dollars to have a national postal service” which works out to costing about $25 a year per person (population of Canada in 2024 = 40 million). Seems like a pretty reasonable cost to me.

Julie S. Lalonde @julieslalonde.bsky.social · 13d

"Canada Post is on track to lose money" Hum. Duh. It cost less than a toonie to send a letter across Canada.

"Canada Post is a service and not a business" was common knowledge until late stage capitalism brain rotted most people into think if it ain't making money for shareholders, it's failing.

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 12d

I'd be happy to give a (virtual) talk to your lab or department or whatever if you think that would help. I have a talk specifically for this topic.

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Lars Fischer @fischblog.bsky.social · 15d

This is not because AI is generally useless. It is a tool that has to be carefully tested for possible uses and implemented in ways that create a net benefit. Like all tools. This is not happening.
So it may point to the possibility that a lot of business decision makers are useless.

Ethan White @ethanwhite.weecology.org · 15d

"No single study on AI in the workplace is going to be definitive, but evidence is mounting that AI is affecting people’s work in the same way it’s affecting everything else: It is making it easier to output low-quality slop that other people then have to wade through."

AI ‘Workslop’ Is Killing Productivity and Making Workers Miserable

AI slop is taking over workplaces. Workers said that they thought of their colleagues who filed low-quality AI work as "less creative, capable, and reliable than they did before receiving the output."

www.404media.co

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Ian / Sìne @vonaether.bsky.social · 16d

People are making Rapture jokes like there's no tomorrow

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Jeremy Labrecque 🇨🇦 @jeremylabrecque.bsky.social · 15d

The audience is health scientists so I wouldn’t consider causal effect to be jargon. But even among a general audience I don’t think “genuine relationship“ would be clearer. but maybe I’m wrong