On Elsevier

Elsevier is the world’s largest and most profitable scientific publisher, making a profit of 1.1 billion dollars on revenue of 3.2 billion dollars in 2009. Elsevier have also been involved in many dubious practices, including the publishing of fake medical journals sponsored by pharmaceutical companies, and the publication of what are most kindly described as extraordinarily shoddy journals. Until 2009, parent company Reed Elsevier helped facilitate the international arms trade. (This is just a tiny sample: for more, see Gowers’s blog post, or look at some of the links on this page.) For this, executives at Reed Elsevier are paid multi-million dollar salaries (see, e.g., 1 and 2, and links therein).

All this is pretty widely known in the scientific community. However, Tim Gowers recently started a large-scale discussion of Elsevier by scientists, by blogging to explain that he will no longer be submitting papers to Elsevier journals, refereeing for Elsevier, or otherwise supporting the company in any way. The post now has more than 120 comments, with many mathematicians and scientists voicing similar concerns.

Following up from the discussion on Gowers’s post, Tyler Neylon has created a website called The Cost of Knowledge (see also Gowers’s followup) where researchers can declare their unwillingness to “support any Elsevier journal unless they radically change how they operate”. If you’re a mathematician or scientist who is unhappy with Elsevier’s practices, then consider signing the declaration. And while you’re at it, consider making your scientific papers open access, either by depositing them into open repositories such as the arXiv, or by submitting them to open access journals such as the Public Library of Science. Or do both.

If correlation doesn’t imply causation, then what does?

That’s the question I address (very partially) in a new post on my data-driven intelligence blog. The post reviews some of the recent work on causal inference done by people such as Judea Pearl. In particular the post describes the elements of a causal calculus developed by Pearl, and explains how the calculus can be applied to infer causation, even when a randomized, controlled experiment is not possible.