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Archive of posts filed under the Causal Inference category.

Discussion with Dan Kahan on political polarization, partisan information processing. And, more generally, the role of theory in empirical social science

It all began with this message from Dan Kahan, a law professor who does psychology experiments:

Using the aggregate of the outcome variable as a group-level predictor in a hierarchical model

When I was a kid I took a writing class, and one of the assignments was to write a 1-to-2 page story. I can’t remember what I wrote, but I do remember the following story from one of the other kids. In its entirety: I snuck into this pay toilet and I can’t get out! […]

Classical probability does not apply to quantum systems (causal inference edition)

James Robins, Tyler VanderWeele, and Richard Gill write: Neyman introduced a formal mathematical theory of counterfactual causation that now has become standard language in many quantitative disciplines, but not in physics. We use results on causal interaction and interference between treatments (derived under the Neyman theory) to give a simple new proof of a well-known […]

More on Bayesian methods and multilevel modeling

Ban Chuan Cheah writes:

Is coffee a killer? I don’t think the effect is as high as was estimated from the highest number that came out of a noisy study

Thomas Lumley writes: The Herald has a story about hazards of coffee. The picture caption says Men who drink more than four cups a day are 56 per cent more likely to die. which is obviously not true: deaths, as we’ve observed before, are fixed at one per customer.  The story says It’s not that people […]

Does it matter that a sample is unrepresentative? It depends on the size of the treatment interactions

In my article about implausible p-values in psychology studies, I wrote: “Women Are More Likely to Wear Red or Pink at Peak Fertility,” by Alec Beall and Jessica Tracy, is based on two samples: a self-selected sample of 100 women from the Internet, and 24 undergraduates at the University of British Columbia. . . . […]

All inference is about generalizing from sample to population

Jeff Walker writes: Your blog has skirted around the value of observational studies and chided folks for using causal language when they only have associations but I sense that you ultimately find value in these associations. I would love for you to expand this thought in a blog. Specifically: Does a measured association “suggest” a […]

Learning about correlations using cross-sectional and over-time comparisons between and within countries

Antonio Rinaldi writes: Here in Italy an “hype” topic is the “staffetta tra generazioni”, handover between generations: since unemployment rate in young people is very high, someone in the government is thinking to encourage older people to anticipate their retirement to make more jobs available for youngs. I am not an economist and I don’t […]

Test scores and grades predict job performance (but maybe not at Google)

Eric Loken writes: If you’re used to Google upending conventional wisdom, then yesterday’s interview with Laszlo Bock in the New York Times did not disappoint. Google has determined that test scores and transcripts are useless because they don’t predict performance among its employees. . . . I [Loken] am going to assume they’re well aware […]

I doubt they cheated

Following up on my regression-discontinuity post from the other day, Brad DeLong writes: The feel (and I could well be wrong) as that at some point somebody said: “This is very important, but it won’t get published without a statistically significant headline finding. Torture the data via specification search until we find a statistically significant […]