As a data analyst and a scientist, Fisher > Neyman, no question. But as a theorist, Fisher came up with ideas that worked just fine in his applications but can fall apart when people try to apply them too generally. Here’s an example that recently came up. Deborah Mayo pointed me to a comment by [...]The post In which I side with Neyman over Fisher appeared first on Statistical Modeling,…
While I was waiting for my coffee this morning, I flipped through the newspapers on one of the tables in my local coffee place when my eye got caught by this article in The Times (I think to see the full article you need a subscription $-$ which I...
At the City University of New York Graduate Center, 365 Fifth Avenue (between 34th and 35th street), room 6002. The topic: causality and statistical learning. Announcement is here (scroll down). It says that if you would like to attend any event, please respond by emailing datamining@gc.cuny.edu I’m also giving a shorter talk on the same [...]The post My talk midtown this Friday noon (and at Columbia Monday afternoon) appeared first…
Choices in Visualizing Data This time, it’s not at the university, it’s at a data science meetup. Here are the slides. I actually prefer the term “statistical graphics” or “visualizing quantitative information” rather than “visualizing data.” I spend a lot of time graphing inferences and fitted models, understanding my fits and doing exploratory model analysis. [...]The post My talk in Chicago this Thurs 6:30pm appeared first on Statistical Modeling, Causal…
A couple people pointed me to a recent article, “Detecting Causality in Complex Ecosystems,” by fisheries researchers George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael Fogarty, and Stephan Munch. I don’t know anything about ecology research but I could imagine this method being useful in that field. I can’t see the approach [...]The post Detecting predictability in complex ecosystems appeared first on Statistical Modeling, Causal Inference, and…
Mike Zyphur writes: Kaggle.com has launched a competition to determine what’s an effect and what’s a cause. They’ve got correlated variables, they’re deprived of context, and you’re asked to determine the causal order. $5,000 prizes. I followed the link and the example they gave didn’t make much sense to me (the two variables were temperature [...]The post Can you write a program to determine the causal order? appeared first on…
Causality and Statistical Learning Andrew Gelman, Statistics and Political Science, Columbia University Wed 27 Mar, 4pm, Betty Ford Auditorium, Ford School of Public Policy 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 [...]
We finally got around to prepare everything we needed to advertise the position which will be available in the MRC grant we've been awarded last year.The project will run for 30 months and we're looking for a post-doctoral candidate to work on the Rese...
In the biomedical research world, stakes don't get any higher than randomized trials, where no stone goes unturned. The notion of confounding and baseline imbalance is a frequent source of heartburn, especially for statisticians, as they're often asked...
The title of this post is silly but I have an important point to make, regarding an implicit model which I think many people assume even though it does not really make sense. Following a link from Sanjay Srivastava, I came across a post from David Funder saying that it’s useful to talk about the [...]
