The Long, Cruel History of the Anti-Abortion Crusade [reposted]

[Excerpts from an editorial in the NYT of John Irving, American author of the Cider House Rules novel we enjoyed reading 30 years ago]

“(…) I respect your personal reasons not to have an abortion — no one is forcing you to have one. I respect your choice. I’m pro-choice — often called pro-abortion by the anti-abortion crusaders, although no one is pro-abortion. What’s unequal about the argument is the choice; the difference between pro-life and pro-choice is the choice. Pro-life proponents have no qualms about forcing women to go through childbirth — they give women no choice (…)

I must remind the Roman Catholic Church of the First Amendment to the United States Constitution: “Congress shall make no law respecting an establishment of religion, or prohibiting the free exercise thereof.” In other words, we are free to practice the religion of our choice, and we are protected from having someone else’s religion practiced on us. Freedom of religion in the United States also means freedom from religion (…)

The prevailing impetus to oppose abortion is to punish the woman who doesn’t want the child. The sacralizing of the fetus is a ploy. How can “life” be sacred (and begin at six weeks, or at conception), if a child’s life isn’t sacred after it’s born? Clearly, a woman’s life is never sacred; as clearly, a woman has no reproductive rights (…)

Of an unmarried woman or girl who got pregnant, people of my grandparents’ generation used to say: “She is paying the piper.” Meaning, she deserves what she gets — namely, to give birth to a child. That cruelty is the abiding impetus behind the dishonestly named right-to-life movement. Pro-life always was (and remains) a marketing term. Whatever the anti-abortion crusaders call themselves, they don’t care what happens to an unwanted child — not after the child is born — and they’ve never cared about the mother.”

Stein’s method in machine learning [workshop]

There will be an ICML workshop on Stein’s method in machine learning & statistics, next July 14 or 15, located in Long Beach, CA. Organised by François-Xavier Briol (formerly Warwick), Lester Mckey, Chris Oates (formerly Warwick), Qiang Liu, and Larry Golstein. To quote from the webpage of the workshop

Stein’s method is a technique from probability theory for bounding the distance between probability measures using differential and difference operators. Although the method was initially designed as a technique for proving central limit theorems, it has recently caught the attention of the machine learning (ML) community and has been used for a variety of practical tasks. Recent applications include goodness-of-fit testing, generative modeling, global non-convex optimisation, variational inference, de novo sampling, constructing powerful control variates for Monte Carlo variance reduction, and measuring the quality of Markov chain Monte Carlo algorithms.

Speakers include Anima Anandkumar, Lawrence Carin, Louis Chen, Andrew Duncan, Arthur Gretton, and Susan Holmes. I am quite sorry to miss two workshops dedicated to Stein’s work in a row, the other one being at NUS, Singapore, around the Stein paradox.

BayesComp 20: call for contributed sessions!

Just to remind readers of the incoming deadline for BayesComp sessions:

The deadline for providing a title and brief abstract that the session is April 1, 2019. Please provide the names and affiliations of the organizer and the three speakers (the organizer can be one of them). Each session lasts 90 minutes and each talk should be 30 minutes long including Q&A. Contributed sessions can also consist of tutorials on the use of novel software. Decisions will be made by April 15, 2019. Please send your proposals to Christian Robert, co-chair of the scientific committee. We look forward to seeing you at BayesComp 20!

In case you do not feel like organising a whole session by yourself, contact the ISBA section you feel affinity with and suggest it helps building this session together!

call for sessions and labs at Bay2sC0mp²⁰

A call to all potential participants to the incoming BayesComp 2020 conference at the University of Florida in Gainesville, Florida, 7-10 January 2020, to submit proposals [to me] for contributed sessions on everything computational or training labs [to David Rossell] on a specific language or software. The deadline is April 1 and the sessions will be selected by the scientific committee, other proposals being offered the possibility to present the associated research during a poster session [which always is a lively component of the conference]. (Conversely, we reserve the possibility of a “last call” session made from particularly exciting posters on new topics.) Plenary speakers for this conference are

• David Blei (Columbia University)
• Paul Fearnhead (University of Lancaster)
• Emily Fox (University of Washington)
• Max Welling (University of Amsterdam)

and the first invited sessions are already posted on the webpage of the conference. We dearly hope to attract a wide area of research interests into a as diverse as possible program, so please accept this invitation!!!

US elections [xkcd repost]

Randall Munroe of xkcd has designed this massive map of the US elections with links to all candidates and random places that I first mistook for voting stations (this would have been great!, to highlight how would-be voters can be discouraged from voting in some districts), as well as a few hidden comics and places irrelevant for the elections. (Warning: The above is a static copy!) With the great subtext that to edit the map readers need to submit their ballot before on November 7th.

running shoes

A few days ago, when back from my morning run, I spotted a NYT article on Nike shoes that are supposed to bring on average a 4% gain in speed. Meaning for instance a 3 to 4 minute gain in a half-marathon.

“Using public race reports and shoe records from Strava, a fitness app that calls itself the social network for athletes, The Times found that runners in Vaporflys ran 3 to 4 percent faster than similar runners wearing other shoes, and more than 1 percent faster than the next-fastest racing shoe.”

What is interesting in this NYT article is that the two journalists who wrote it have analysed their own data, taken from Strava. Using a statistical model or models (linear regression? non-linear regression? neural net?) to predict the impact of the shoe make, against “all” other factors contributing to the overall time or position or percentage gain or yet something else. In most analyses produced in the NYT article, the 4% gain is reproduced (with a 2% gain for female shoe switcher and a 7% gain for slow runners).

“Of course, these observations do not constitute a randomized control trial. Runners choose to wear Vaporflys; they are not randomly assigned them. One statistical approach that seeks to address this uses something called propensity scores, which attempt to control for the likelihood that someone wears the shoes in the first place. We tried this, too. Our estimates didn’t change.”

The statistical analysis (or analyses) seems rather thorough, from what is reported in the NYT article, with several attempts at controlling for confounders. Still, the data itself is observational, even if providing a lot of variables to run the analyses, as it only covers runners using Strava (from 5% in Tokyo to 25% in London!) and indicating the type of shoes they wear during the race. There is also the issue that the shoes are quite expensive, at $250 a pair, especially if the effect wears out after 100 miles (this was not tested in the study), as I would hesitate to use them unless the race conditions look optimal (and they never do!). There is certainly a new shoes effect on top of that, between the real impact of a better response and a placebo effect. As shown by a similar effect of many other shoe makes. Hence, a moderating impact on the NYT conclusion that these Nike Vaporflys (flies?!) are an “outlier”. But nonetheless a fairly elaborate and careful statistical study that could potentially make it to a top journal like Annals of Applied Statistics!

precisely!