Starting the Conversation on Models with Alyssa Bilinski | Season 5 Episode 11
Casual Inference
Alyssa Bilinski, Peterson Family Assistant Professor of Health Policy, and Assistant Professor of Biostatistics, at Brown University School of Public Health. Her research focuses on developing novel methods for policy evaluation and applying these to identify interventions that most efficiently improve population health and well-being. Episode notes: PNAS paper: Shuo Feng’s pre-print: Our uncertainty paper: Follow along on Twitter: Alyssa: The American Journal of Epidemiology: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of Edited by
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Flexible methods with Edward Kennedy | Season 5 Episode 10
Casual Inference
Edward Kennedy Associate Professor, Department of Statistics & Data Science, Carnegie Mellon. Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California: Doubly Robust Capture-Recapture Methods for Estimating Population Size: Follow along on Twitter: The American Journal of Epidemiology: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of Edited by
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What Sports and Feminism can tell us about Causal Inference with Sheree Bekker & Stephen Mumford | Season 5 Episode 9
Casual Inference
Sheree Bekker & Stephen Mumford are Co-directors of the Feminist Sport Lab and have a book coming soon: “Open Play: the case for feminist sport”, coming Spring 2025. Reaktion Books (UK), University of Chicago Press (US). Sheree Bekker: Associate Professor, University of Bath, , Stephen Mumford, Professor of Metaphysics, A Author of Dispositions (Oxford, 1998), Russell on Metaphysics (Routledge, 2003), Laws in Nature (Routledge, 2004), David Armstrong (Acumen, 2007), Watching Sport: Aesthetics, Ethics and Emotion (Routledge, 2011), Getting Causes from Powers (Oxford,...
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Observational Causal Analyses with Erick Scott | Season 5 Episode 8
Casual Inference
Erick Scott is founder of cStructure, a causal science startup. Erick has expertise in medicine, public health, and computational biology. [email protected] “A causal roadmap for generating high-quality real-world evidence” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603361/ Follow along on Twitter: The American Journal of Epidemiology: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of Edited by
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Friends Let Friends Do Mediation Analysis with Nima Hejazi | Season 5 Episode 7
Casual Inference
Nima Hejazi is an assistant professor in biostatistics at Harvard University. His methodological work often draws upon tools and ideas from semi- and non-parametric inference, high-dimensional and large-scale inference, targeted or debiased machine learning (e.g., targeted minimum loss estimation, method of sieves), and computational statistics. Surprised by the Hot Hand Fallacy? A Truth in the Law of Small Numbers by Joshua B. Miller & Adam Sanjurjo: Nima is on Twitter/X as @nshejazi () and my academic webpage is Recent translational review paper (intended for the infectious...
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Fun and Game(s) Theory with Aaditya Ramdas | Season 5 Episode 6
Casual Inference
Aaditya Ramdas is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. His research interests include game-theoretic statistics and sequential anytime-valid inference, multiple testing and post-selection inference, and uncertainty quantification for machine learning (conformal prediction, calibration). His applied areas of interest include neuroscience, genetics and auditing (real-estate, finance, elections). Aaditya received the IMS Peter Gavin Hall Early Career Prize, the COPSS Emerging Leader Award, the Bernoulli New Researcher Award,...
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Cookies, Causal Inference, and Careers with Ingrid Giesinger #Epicookiechallenge | Season 5 Episode 5
Casual Inference
Ingrid is a doctoral student in Epidemiology at the Dalla Lana School of Public Health at the University of Toronto. Follow along on Twitter: The American Journal of Epidemiology: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of Edited by
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Analyzing the Analysts: Reproducibility with Nick Huntington-Klein | Season 5 Episode 4
Casual Inference
Nick Huntington-Klein is an Assistant Professor, Department of Economics, Albers School of Business and Economics, Seattle University. His research focus is econometrics, causal inference, and higher education policy. He’s also the author of an introductory causal inference textbook called The Effect and the creator of a number of Stata packages for implementing causal effect estimation procedures. Nick’s book, online version: The Paper of How: Nick’s twitter & BlueSky: @nickchk Nick’s website: Follow along on Twitter: The American Journal of Epidemiology: ...
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Immortal Time Bias | Season 5 Episode 3
Casual Inference
Lucy and Ellie chat about immortal time bias, discussing a new paper Ellie co-authored on clone-censor-weights. The Clone-Censor-Weight Method in Pharmacoepidemiologic Research: Foundations and Methodological Implementation: Immortal time in pregnancy: Follow along on Twitter: The American Journal of Epidemiology: Ellie: Lucy: 🎶 Our intro/outro music is courtesy of Edited by
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Targeted Learning with Mar van der Laan | Season 5 Episode 2
Casual Inference
Mark van der Laan is a professor of statistics at the University of California, Berkeley. His research focuses on developing statistical methods to estimate causal and non-causal parameters of interest, based on potentially complex and high dimensional data from randomized clinical trials or observational longitudinal studies, or from cross-sectional studies. Center for Targeted Learning, Berkeley: A causal roadmap: Short course on causal learning: Handbook on the TLverse (Targeted Learning in R): Mark on twitter: Follow along on Twitter: The American...
info_outlineEllie Murray and Lucy D’Agostino McGowan chat with Ralph D’Agostino Sr. and Ralph D’Agostino Jr. about their careers in statistics, looking back at how things have developed and forward at where they see the world of statistics and epidemiology going.
Ralph D’Agostino Sr. was a professor of Mathematics/Statistics, Biostatistics, and Epidemiology at Boston University. He was the lead biostatistician for the Framingham Heart Study, a biostatistical consultant to The New England Journal of Medicine, an editor of Statistics in Medicine and lead editor of their Tutorials, and a member and consultant on FDA committees. His major fields of research were clinical trials, prognostic models, longitudinal analysis, multivariate analysis, robustness, and outcomes/effectiveness research.
Ralph D’Agostino Jr. is a professor in the Department of Biostatistics and Data Science at Wake Forest University where he is the Director of the Biostatistics Core of the Comprehensive Cancer Center. Methodologically his research includes developing statistical techniques for evaluating data from observational settings, handling missing data in applied problems, and developing predictive functions to identify prospectively patients at elevated risk for future negative outcomes. Some of his recent work includes the development of methods using propensity score models to identify safety signals in large retrospective databases.