James Watson (no, not the one who said that cancer would be cured by 2000, and not this guy either) writes: You may have seen the paper that came out on Friday in the Lancet on hydroxychloroquine/chloroquine in COVID19 hospitalised patients. It’s got quite a lot of media attention already. This is a retrospective study […]
Archive of posts filed under the Causal Inference category.
New report on coronavirus trends: “the epidemic is not under control in much of the US . . . factors modulating transmission such as rapid testing, contact tracing and behavioural precautions are crucial to offset the rise of transmission associated with loosening of social distancing . . .”
Juliette Unwin et al. write: We model the epidemics in the US at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the time-varying reproduction number (the average number of secondary infections caused by an infected person), the number of individuals that have been infected and […]
This one’s important: Designing clinical trials for coronavirus treatments and vaccines
I’ve had various thoughts regarding clinical trials for coronavirus treatments and vaccines, and then I came across thoughtful posts by Thomas Lumley and Joseph Delaney on vaccines. So let’s talk, first about treatments, then about vaccines. Clinical trials for treatments The first thing I want to say is that designing clinical trials is not just […]
If the outbreak ended, does that mean the interventions worked? (Jon Zelner talk tomorrow)
Jon Zelner speaks tomorrow (Thurs) at 1pm: PREDICTING COVID-19 TRANSMISSION In this talk Dr. Zelner will discuss some ongoing modeling work focused on understanding when we can and cannot infer that interventions meant to stop or slow infectious disease transmission have actually worked, and when observed outcomes cannot be distinguished from selection bias. Dude’s an […]
“Positive Claims get Publicity, Refutations do Not: Evidence from the 2020 Flu”
Part 1 Andrew Lilley, Gianluca Rinaldi, and Matthew Lilley write: You might be familiar with a recent paper by Correira, Luck, and Verner who argued that cities that enacted non-pharmaceutical interventions earlier / for longer during the Spanish Flu of 1918 had higher subsequent economic growth. The paper has had extensive media coverage – e.g. […]
Imperial College report on Italy is now up
See here. Please share your reactions and suggestions in comments. I’ll be talking with Seth Flaxman tomorrow, and we’d appreciate all your criticisms and suggestions. All this is important not just for Italy but for making sensible models to inform policy all over the world, including here.
NPR’s gonna NPR (special coronavirus junk science edition)
1. The news! Zad’s cat, pictured above, is not impressed by this bit of cargo-cult science that two people sent to me: No vaccine or effective treatment has yet been found for people suffering from COVID-19. Under the circumstances, a physician in Kansas City wonders whether prayer might make a difference, and he has launched […]
10 on corona
Here are some things people have sent me lately. They are in no particular order, except that I put the last item last so we could end with some humor. After this, I’ll write a few more blog posts, then it’ll be time to do some real work. Table of contents 1. Suspicious coronavirus numbers […]
Information or Misinformation During a Pandemic: Comparing the effects of following Nassim Taleb, Richard Epstein, or Cass Sunstein on twitter.
So, there’s this new study doing the rounds. Some economists decided to study the twitter followers of prominent coronavirus skeptics and fearmongers, and it seems that followers of Nassim Taleb were more likely to shelter in place, and less like to die of coronavirus, than followers of Richard Epstein or Cass Sunstein. And the differences […]
New analysis of excess coronavirus mortality; also a question about poststratification
Uros Seljak writes: You may be interested in our Gaussian Process counterfactual analysis of Italy mortality data that we just posted. Our results are in a strong disagreement with the Stanford seropositive paper that appeared on Friday. Their work was all over the news, but is completely misleading and needs to be countered: they claim […]
Come up with a logo for causal inference!
Stephen Cole, Jennifer Hill, Luke Keele, Ilya Shpitser, and Dylan Small write: We wanted to provide an update on our efforts to build the Society for Causal Inference (SCI). As you may recall, we are creating the SCI as a home for causal inference research that will increase support and knowledge sharing both within the […]
I’m still struggling to understand hypothesis testing . . . leading to a more general discussion of the role of assumptions in statistics
I’m sitting at this talk where Thomas Richardson is talking about testing the hypothesis regarding a joint distribution of three variables, X1, X2, X3. The hypothesis being tested is that X1 and X2 are conditionally independent given X3. I don’t have a copy of Richardson’s slides, but here’s a paper that I think it related, […]
Online Causal Inference Seminar starts next Tues!
Dominik Rothenhäusler writes: We are delighted to announce the creation of the Online Causal Inference Seminar (OCIS)! Our goal in creating this seminar series is to provide a platform for our community to continue interacting and growing in spite of the current health crisis. The causal tent is a big one, and we hope to […]
Are we ready to move to the “post p < 0.05 world”?
Robert Matthews writes: Your post on the design and analysis of trials really highlights how now more than ever it’s vital the research community takes seriously all that “nit-picking stuff” from statisticians about the dangers of faulty inferences based on null hypothesis significance testing. These dangers aren’t restricted to the search for new therapies. I’m […]
Some recommendations for design and analysis of clinical trials, with application to coronavirus
Various people have been contacting me lately about recommendations for design and analysis of clinical trials, with application to coronavirus. Below are some quick thoughts, or you can scroll down to the Summary Recommendations at the end. I’m sure there’s lots more to say on this topic but I’ll get my quick thoughts down here. […]
New dataset: coronavirus tracking using data from smart thermometers
Dan Keys writes: I recently came across the new coronavirus tracker website which is based on data from Kinsa smart thermometers. Whenever someone takes their temperature with one of these thermometers, the data is sent to Kinsa. Thermometer users also input their location, age, and gender. The company has been using these data for a […]
Is it really true that candidates who are perceived as ideologically extreme do even worse if “they actually pose as more radical than they really are”?
Most of Kruggy’s column today is about macroeconomics, a topic I’m pretty much ignorant of. But I noticed one political science claim: It’s easy to make the political case that Democrats should nominate a centrist, rather than someone from the party’s left wing. Candidates who are perceived as ideologically extreme usually pay an electoral penalty; […]
MRP Conference at Columbia April 3rd – April 4th 2020
The Departments of Statistics and Political Science and Institute for Social and Economic Research and Policy at Columbia University are delighted to invite you to our Spring conference on Multilevel Regression and Poststratification. Featuring Andrew Gelman, Beth Tipton, Jon Zelner, Shira Mitchell, Qixuan Chen and Leontine Alkema, the conference will combine a mix of cutting […]
Causal inference in AI: Expressing potential outcomes in a graphical-modeling framework that can be fit using Stan
David Rohde writes: We have been working on an idea that attempts to combine ideas from Bayesian approaches to causality developed by you and your collaborators with Pearl’s do calculus. The core idea is simple, but we think powerful and allows some problems previously that only had known solutions with the do calculus to be […]
The latest Perry Preschool analysis: Noisy data + noisy methods + flexible summarizing = Big claims
Dean Eckles writes: Since I know you’re interested in Heckman’s continued analysis of early childhood interventions, I thought I’d send this along: The intervention is so early, it is in their parents’ childhoods. See the “Perry Preschool Project Outcomes in the Next Generation” press release and the associated working paper. The estimated effects are huge: […]