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Archive of posts filed under the Causal Inference category.

Seth Roberts

I met Seth back in the early 1990s when we were both professors at the University of California. He sometimes came to the statistics department seminar and we got to talking about various things; in particular we shared an interest in statistical graphics. Much of my work in this direction eventually went toward the use […]

Crowdstorming a dataset

Raphael Silberzahn writes: Brian Nosek, Eric Luis Uhlmann, Dan Martin, and I just launched a project through the Open Science Center we think you’ll find interesting. The basic idea is to “Crowdstorm a Dataset”. Multiple independent analysts are recruited to test the same hypothesis on the same data set in whatever manner they see as […]

Big Data…Big Deal? Maybe, if Used with Caution.

This post is by David K. Park As we have witnessed, the term “big data” has been thrusted onto the zeitgeist in the past several years, however, when one pushes beyond the hype, there seems to be little substance there. We’ve always had “data” so what so unique about it this time? Yes, we recognize it’s […]

Adjudicating between alternative interpretations of a statistical interaction?

Jacob Felson writes: Say we have a statistically significant interaction in non-experimental data between two continuous predictors, X and Z and it is unclear which variable is primarily a cause and which variable is primarily a moderator. One person might find it more plausible to think of X as a cause and Z as a […]

References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling

A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could […]

New research journal on observational studies

Dylan Small writes: I am starting an observational studies journal that aims to publish papers on all aspects of observational studies, including study protocols for observational studies, methodologies for observational studies, descriptions of data sets for observational studies, software for observational studies and analyses of observational studies. One of the goals of the journal is […]

In the best alternative histories, the real world is what’s ultimately real

This amusing-yet-so-true video directed by Eléonore Pourriat shows a sex-role-reversed world where women are in charge and men don’t get taken seriously. It’s convincing and affecting, but the twist that interests me comes at the end, when the real world returns. It’s really creepy. And this in turn reminds me of something we discussed here […]

“Edlin’s rule” for routinely scaling down published estimates

A few months ago I reacted (see further discussion in comments here) to a recent study on early childhood intervention, in which researchers Paul Gertler, James Heckman, Rodrigo Pinto, Arianna Zanolini, Christel Vermeerch, Susan Walker, Susan M. Chang, and Sally Grantham-McGregor estimated that a particular intervention on young children had raised incomes on young adults […]

My talks in Bristol this Wed and London this Thurs

1. Causality and statistical learning (Wed 12 Feb 2014, 16:00, at University of Bristol): Causal inference is central to the social and biomedical sciences. There are unresolved debates about the meaning of causality and the methods that should be used to measure it. As a statistician, I am trained to say that randomized experiments are […]

Keli Liu and Xiao-Li Meng on Simpson’s paradox

XL sent me this paper, “A Fruitful Resolution to Simpson’s Paradox via Multi-Resolution Inference.” I told Keli and Xiao-Li that I wasn’t sure I fully understood the paper—as usual, XL is subtle and sophisticated, also I only get about half of his jokes—but I sent along these thoughts: 1. I do not think counterfactuals or […]