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Archive of posts filed under the Causal Inference category.

A whole fleet of gremlins: Looking more carefully at Richard Tol’s twice-corrected paper, “The Economic Effects of Climate Change”

We had a discussion the other day of a paper, “The Economic Effects of Climate Change,” by economist Richard Tol. The paper came to my attention after I saw a notice from Adam Marcus that it was recently revised because of data errors. But after looking at the paper more carefully, I see a bunch […]

How much can we learn about individual-level causal claims from state-level correlations?

Hey, we all know the answer: “correlation does not imply causation”—but of course life is more complicated than that. As philosophers, economists, statisticians, and others have repeatedly noted, most of our information about the world is observational not experimental, yet we manage to draw causal conclusions all the time. Sure, some of these conclusions are […]

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 […]

Into the thicket of variation: More on the political orientations of parents of sons and daughters, and a return to the tradeoff between internal and external validity in design and interpretation of research studies

We recently considered a pair of studies that came out awhile ago involving children and political orientation: Andrew Oswald and Nattavudh Powdthavee found that, in Great Britain, parents of girls were more likely to support left-wing parties, compared to parents of boys. And, in the other direction, Dalton Conley and Emily Rauscher found with survey […]

Postdoc with Liz Stuart on propensity score methods when the covariates are measured with error

Liz Stuart sends this one along:

Judea Pearl overview on causal inference, and more general thoughts on the reexpression of existing methods by considering their implicit assumptions

This material should be familiar to many of you but could be helpful to newcomers. Pearl writes: ALL causal conclusions in nonexperimental settings must be based on untested, judgmental assumptions that investigators are prepared to defend on scientific grounds. . . . To understand what the world should be like for a given procedure to […]

San Fernando Valley cityscapes: An example of the benefits of fractal devastation?

I know we have some readers in the L.A. area and you might be interested in a comment on our recent post regarding the beneficial (in a Jane Jacobs sense) effects of selective devastation of micro-neighborhoods in a city. I gave the example of London after the fractal effects of bombing in WW2, and BMGM […]


There’s lots of overlap but I put each paper into only one category.  Also, I’ve included work that has been published in 2013 as well as work that has been completed this year and might appear in 2014 or later.  So you can can think of this list as representing roughly two years’ work. Political […]

My talk at Leuven, Sat 14 Dec

Can we use Bayesian methods to resolve the current crisis of unreplicable research? In recent years, psychology and medicine have been rocked by scandals of research fraud. At the same time, there is a growing awareness of serious flaws in the general practices of statistics for scientific research, to the extent that top journals routinely […]

What predicts whether a school district will participate in a large-scale evaluation?

Liz Stuart writes: I am writing to solicit ideas for how we might measure a particular type of political environment, relevant to school districts’ likelihood of participating in federal evaluations (funded by the US Department of Education) of education programs. This is part of a larger project investigating external validity and the generalizability of results […]

Does a professor’s intervention in online discussions have the effect of prolonging discussion or cutting it off?

Usually I don’t post answers to questions right away, but Mark Liberman was kind enough to answer my question yesterday so I think I should reciprocate. Mark asks: I’ve been playing around with data from Coursera transaction logs, for an economics course and a modern poetry course so far. For the Modern Poetry course, where […]