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Archive of posts filed under the Causal Inference category.

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 […]

2013

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 […]

That’s crazy talk!

Tenure track faculty opening at the Center for the Promotion of Research Involving Innovative Statistical Methodology, with Jennifer Hill, Marc Scott, and other world-class researchers. It looks like a great opportunity.

Why ask why? Forward causal inference and reverse causal questions

Guido Imbens and I write: The statistical and econometrics literature on causality is more focused on “effects of causes” than on “causes of effects.” That is, in the standard approach it is natural to study the effect of a treatment, but it is not in general possible to define the causes of any particular outcome. […]

How much do we trust a new claim that early childhood stimulation raised earnings by 42%?

Hal Pashler wrote in about a recent paper, “Labor Market Returns to Early Childhood Stimulation: a 20-year Followup to an Experimental Intervention in Jamaica,” by Paul Gertler, James Heckman, Rodrigo Pinto, Arianna Zanolini, Christel Vermeerch, Susan Walker, Susan M. Chang, and Sally Grantham-McGregor. Here’s Pashler: Dan Willingham tweeted: @DTWillingham: RCT from Jamaica: Big effects 20 […]

Berri Gladwell Loken football update

Sports researcher Dave Berri had a disagreement with a remark in our recent discussion of Malcolm Gladwell. Berri writes: This post [from Gelman] contains the following paragraph: Similarly, when Gladwell claimed that NFL quarterback performance is unrelated to the order they were drafted out of college, he appears to have been wrong. But if you […]

Bing is preferred to Google by people who aren’t like me

This one is fun because I have a double conflict of interest: I’ve been paid (at different times) both by Google and by Microsoft. Here’s the story: Microsoft, September 2012: An independent research company, Answers Research based in San Diego, CA, conducted a study using a representative online sample of nearly 1000 people, ages 18 […]

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. . . . […]