Adrian Raftery

Pattern scaling: How local warming is related to global warming: An explainer Abstract. In the fourth of an occasional series of explainers on the statistics and data underpinning our understanding of climate change, Adrian E. Rafter

Our explainer ``Pattern scaling: How local warming is related to global warming: An explainer,’’ with @andrewgarrett.bsky.social & David Stephenson, just published in Significance: academic.oup.com/jrssig/artic...

Proud of our article, “Bayesian Projection of Extant Refugee & Asylum Seeker Populations,” with Herb Susmann, just published in Demography.

Well worth a read. Accessible writing on, and analysis of, climate change targets and carbon intensity from @adrianraftery.bsky.social - a member of our @royalstatsoc.bsky.social Climate Change Task Force.

Did the Paris Climate Agreement work? communities.springernature.com/posts/did-th...

Emissions from economic growth undermine international progress on climate change, University of Washington study says A study led by the University of Washington found that while the decade-old Paris Agreement has made some progress in fighting climate change, those gains have been outweighed by emissions associated ...

I was interviewed on @obp.org ’s Think Out Loud by Dave Miller about our study on climate trends since the 2015 Paris Agreement: www.opb.org/article/2025.... The study is at www.nature.com/articles/s43...

Mitigation efforts to reduce carbon dioxide emissions and meet the Paris Agreement have been offset by economic growth - Communications Earth & Environment Global carbon dioxide intensity declined from 2015 to 2024 following the Paris Agreement, but total emissions still increased due to economic growth, according to a global analysis of population, gros...

Our article “Mitigation efforts to reduce carbon dioxide emissions and meet the Paris Agreement have been offset by economic growth” www.nature.com/articles/s43..., w Jitong Jiang & Skylar Shi just published. See also the @uwnews.uw.edu release www.washington.edu/news/2025/10...

Optimal pandemic control strategies and cost-effectiveness of COVID-19 non-pharmaceutical interventions in the United States - BMC Global and Public Health Background Non-pharmaceutical interventions (NPIs) in response to the COVID-19 pandemic necessitated a trade-off between the health impacts of viral spread and the social and economic costs of restric...

1/ Contrary to recent claims by two political scientists, new work by @adrianraftery.bsky.social & Nick Irons estimates that COVID non-pharmacuetical interventions (NPIs) saved an estimated 860,000 lives in the US *in 2020 alone*.

bmcglobalpublichealth.biomedcentral.com/articles/10....

I'm delighted to serve as Chair-Elect for this new section, along with Chair @adrianraftery.bsky.social, Programme chair @nialfriel.bsky.social, Treasurer @monjalexander.bsky.social, and Secretary EJ Wagenmakers.

I'm looking forward to building this section and serving the community!

I'm super excited to announce that ISBA @isba-bayesian.bsky.social has voted to start a new section on Bayesian Social Sciences! It will be a great way to further collaborations with many disciplines in the Social Sciences and Humanities.

bss-isba.github.io

It will sponsor the 3rd Workshop on Bayesian Methods for the Social Sciences (BMSS-3) in Dublin, December 9-11, 2026.

The new Bayesian Social Sciences section of @isba-bayesian.bsky.social has just been created: bss-isba.github.io. The committee is myself as chair, @robinryder.bsky.social, chair elect from 2027, @nialfriel.bsky.social, program chair, @monjalexander.bsky.social, Treasurer, EJWagenmakers, Secretary.

Optimal pandemic control strategies and cost-effectiveness of COVID-19 non-pharmaceutical interventions in the United States - BMC Global and Public Health Background Non-pharmaceutical interventions (NPIs) in response to the COVID-19 pandemic necessitated a trade-off between the health impacts of viral spread and the social and economic costs of restric...

Our paper, “Optimal pandemic control strategies …” bmcglobalpublichealth.biomedcentral.com/articles/10.... w @nickirons.bsky.social, just published.Takeaway: U.S. COVID-19 school closures were not cost-effective, but other measures were. medicalxpress.com/news/2025-09...

Multiple imputation of hierarchical nonlinear time series data with an application to school enrollment data International comparisons of hierarchical time series data sets based on survey data, such as annual country-level estimates of school enrollment rates, can suffer from large amounts of missing data due to differing coverage of surveys across countries and across times. A popular approach to handling missing data in these settings is through multiple imputation, which can be especially effective when there is an auxiliary variable that is strongly predictive of and has a smaller amount of missing data than the variable of interest. However, standard methods for multiple imputation of hierarchical time series data can perform poorly when the auxiliary variable and the variable of interest have a nonlinear relationship. Performance can also suffer if the multiple imputations are used to estimate an analysis model that makes different assumptions about the data compared to the imputation model, leading to uncongeniality between analysis and imputation models. We propose a Bayesian method for multiple imputation of hierarchical nonlinear time series data that uses a sequential decomposition of the joint distribution and incorporates smoothing splines to account for nonlinear relationships between variables. We compare the proposed method with existing multiple imputation methods through a simulation study and an application to secondary school enrollment data. We find that the proposed method can lead to substantial performance increases for estimation of parameters in uncongenial analysis models and for prediction of individual missing values.

Our article “Multiple imputation of hierarchical nonlinear time series data with an application to school enrollment data” w Daphne Liu just published in Annals of Applied Statistics: projecteuclid.org/journals/ann... (free preprint arxiv.org/abs/2401.01872)

My website sites.stat.washington.edu/raftery/ just got its annual update, including new publications sites.stat.washington.edu/raftery/Rese...

An explainer of how local warming is related to global warming, from the Royal Statistical Society Climate Change Task Force: rss.org.uk/policy-campa... . See also the detailed article at link.springer.com/article/10.1...

This does involve using R, but I think you could get away with following the manual pretty closely, @adrianraftery.bsky.social's webpage on demographic projections is pretty thorough bayespop.csss.washington.edu

Sign The Open Letter to Stand Up For Science Now! Science is under siege. Trump’s latest Executive Order calls for politically appointed science commissars to evaluate research. Join us in adding your name to our open letter condemning Trump’s escala...

Science is under siege. Trump’s latest Executive Order that appoints politicians — not scientists — to evaluate research. Add your name to the open letter: actionnetwork.org/petitions/op...

A privacy-preserved and high-utility synthesis strategy for risk-based stratified subgroups of the Canadian scleroderma patient registry data Responsible data sharing anchors research reproducibility and promotes the integrity of scientific research. Motivated by Canadian Scleroderma Research Group (CSRG) patient registry data, we present a risk-based method to produce privacy-preserved and high-utility synthetic datasets, which also simultaneously imputes missing data of mixed continuous and categorical types in the original dataset. This method divides all individuals into different subgroups, based on their reidentification risks, and provides tailored synthesis strategies targeted for each risk subgroup, through the associated tuning mechanisms. Under our setting, our risk-based method reduced the number of patients at risk from 198 to four, among the 691 CSRG patients who have no missing values in any of the quasi-identifying variables, while preserving all correct inferential conclusions in the target analysis. The 95% confidence intervals (CIs) have 92.6% overlap, on average, with the CIs constructed using the unperturbed imputation-completed datasets. These findings suggest that our risk-based method makes it possible to release complete synthetic datasets for research reproducibility while ensuring that the reidentification risks are acceptably low. In contrast, the existing one-size-fits-all synthesis strategies that do not take account of different risk levels can lead to unnecessary information loss and possibly incorrect scientific conclusions.

Our paper, "A privacy-preserved and high-utility synthesis strategy for risk-based stratified subgroups of the Canadian scleroderma patient registry data" w Bei Jiang, Russell Steele & Naisyin Wang, just published in Ann Appl Stat: projecteuclid.org/journals/ann...

A call for scientists to stand up for scientific freedom as well as funding: www.nature.com/articles/d41...

Our short course on Subnational Probabilistic Population Projections at PAA 2025 (description attached) will now be hybrid. To ask to join remotely, email raftery@uw.edu from your professional email by April 7 with subject “Join PAA workshop”, saying why you want to.