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Important shift in the clinical literature. NEJM releases new guidelines for statistical reporting of p-values. nejm.org/doi/full/10.10…

Nice talk by Sherri Rose: Towards Standards in Machine Learning. And she is emphasizing the need for teams that cross disciplines, leveraging experience from different areas. Understand applied problem. Respect the analysis. Think about the application.

Many machine learning papers have critical errors and they are accepted anyway in journals, says Sherri Rose; she wrote a nice piece JAMA Network Open on Machine Learning for Prediction in Electronic Health Data. jamanetwork.com/journals/jaman… HMS HCP National Academies

Happy to announce our keynote speakers for CHIL 2020: Yoshua Bengio, Sherri Rose (Sherri Rose), Nigam Shah (Nigam Shah), and Ruslan Salakhutdinov (Russ Salakhutdinov). A reminder that the deadline for papers is in *just over a month* (13th January), see our CFP: chilconference.org/call-for-paper…

Our paper on fair regression is now forthcoming in IBS Biometrics Journal! Biometrics link onlinelibrary.wiley.com/doi/10.1111/bi… ArXiv arxiv.org/abs/1901.10566 Code github.com/zinka88/Fair-R… Discussed next steps needed to bring this work to practice at the recent Int’l Risk Adjustment Network meeting

At a minimum, a #machinelearning model should be reproduced, and ideally replicated, before it is deployed in a clinical setting ja.ma/35m6Aqj

This summer, Sherri Rose will receive the Center for Causal Inference Mid-Career Award for achievements in the development and application of innovative causal inference methods. Dr. Rose will deliver an invited award lecture during the Causal Inference Summer Institute: cceb.med.upenn.edu/cci/2020-summe…

2020 NBER Conference on the Economics of AI — Submission deadline June 1. economicsofai.com/blog/2020/2/3/…

Our new @NBERpubs working paper is out! nber.org/papers/w26736 Fixing undercompensation for several groups in risk adjustment improved fairness for *many other groups and overall fit* We did this by bringing together: 1️⃣fair regression 2️⃣ML for variable selection 3️⃣reinsurance

Registration is open for ACM CHIL 2020! Keynote speakers include Yoshua Bengio of MILA, Sherri Rose of Harvard (Sherri Rose), Nigam Shah of Stanford, Ruslan Salakhutdinov of CMU (Russ Salakhutdinov), and Elaine Nsoesie of Boston University (Dr. Elaine Okanye Nsoesie). Check out chilconference.org/registration/

Welcome to the Farm, Sherri Rose, an expert on statistical machine learning, AI and economics. "I love working on the computational health economics tools I develop because they may have a direct impact on individual lives in the health-care system." stanford.io/3865gLl

Congratulations to HAI faculty member @SherriRose for winning the Mortimer Spiegelman Award – the highest recognition for outstanding contributions to public health statistics. Read more about her work: shar.es/aWHa22

Our new paper, led by Irina Degtiar, develops machine learning estimators for generalizability with observational & randomized data arxiv.org/abs/2109.13288 These methods were motivated by our interest in assessing plan-specific effects on 💲 in Medicaid Code github.com/idegtiar1/CCDS

How to find cause and effect in real-world randomness? A Tiny Lecture, #NobelPrize edition!

What does statistics bring to machine learning & AI? New piece w/Mark van der Laan on why machine learning cannot ignore the lessons of maximum likelihood estimation arxiv.org/abs/2110.12112 Many ML algorithms aren't suited for statistical inference by having deviated from sieve MLEs

Our review of generalizability & transportability led by Irina Degtiar is now published in Annual Reviews: annualreviews.org/doi/abs/10.114… (arXiv: arxiv.org/abs/2102.11904) It synthesizes work across statistics, CS & health while proposing a framework for addressing external validity bias

Woot woot, our generalizability & transportability review is out, co-authored with Sherri Rose! If you'd like to learn more about how to assess and address external validity bias, take a look: annualreviews.org/doi/abs/10.114…, arxiv.org/abs/2102.11904