Casual Inference Lucy D'Agostino McGowan and Ellie Murray
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- Science
Keep it casual with the Casual Inference podcast. Your hosts Lucy D'Agostino McGowan and Ellie Murray talk all things epidemiology, statistics, data science, causal inference, and public health. Sponsored by the American Journal of Epidemiology.
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What Sports and Feminism can tell us about Causal Inference with Sheree Bekker & Stephen Mumford
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, Department for Health,
Centre for Qualitative Research
Centre for Health and Injury and Illness Prevention in Sport
Stephen Mumford, Professor of Metaphysics, Durham University 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, 2011 with Rani Lill Anjum), Metaphysics: a Very Short Introduction (Oxford, 2012) and Causation: a Very Short Introduction (Oxford, 2013 with Rani Lill Anjum). I was editor of George Molnar's posthumous Powers: a Study in Metaphysics (Oxford, 2003) and Metaphysics and Science (Oxford, 2013 with Matthew Tugby). Episode notes:
Feminist Sport Lab: https://www.feministsportlab.com
Causation: A Very Short Introduction by Stephen Mumford & Rani Lill Anjum: https://academic.oup.com/book/616
Faye Norby, Iditarod champion & epidemiologist: https://www.kfyrtv.com/2024/03/28/faye-norby-finishes-iditarod-trail-womens-foot-champion/?outputType=amp
Follow along on Twitter:
The American Journal of Epidemiology: @AmJEpi
Ellie: @EpiEllie
Lucy: @LucyStats
🎶 Our intro/outro music is courtesy of Joseph McDade
Edited by Cameron Bopp -
Observational Causal Analyses with Erick Scott
Erick Scott is founder of cStructure, a causal science startup. Erick has expertise in medicine, public health, and computational biology.
info@cStructure.io
“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: @AmJEpi
Ellie: @EpiEllie
Lucy: @LucyStats
🎶 Our intro/outro music is courtesy of Joseph McDade
Edited by Cameron Bopp -
Friends Let Friends Do Mediation Analysis with Nima Hejazi | Season 5 Episode 7
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: https://www.jstor.org/stable/44955325
Nima is on Twitter/X as @nshejazi (https://twitter.com/nshejazi) and my academic webpage is https://nimahejazi.org
Recent translational review paper (intended for the infectious disease science community) I was involved in describing some causal/statistical frameworks for evaluating immune markers as mediators / surrogate endpoints: https://pubmed.ncbi.nlm.nih.gov/38458870/
The tlverse software ecosystem is on GitHub at https://github.com/tlverse and the tlverse handbook is freely available at https://tlverse.org/tlverse-handbook/
Dr. Hejazi annually co-teaches a causal mediation analysis workshop at SER, and notes from the latest offering are freely available at https://codex.nimahejazi.org/ser2023_mediation_workshop/
Follow along on Twitter:
The American Journal of Epidemiology: @AmJEpi
Ellie: @EpiEllie
Lucy: @LucyStats
🎶 Our intro/outro music is courtesy of Joseph McDade
Edited by Cameron Bopp -
Fun and Game(s) Theory with Aaditya Ramdas
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, the NSF CAREER Award, the Sloan fellowship in Mathematics, and faculty research awards from Adobe and Google. He also spends 20% of his time at Amazon working on causality and sequential experimentation.
Aaditya’s website: https://www.stat.cmu.edu/~aramdas/
Game theoretic statistics resources
Aaditya’s course, Game-theoretic probability, statistics, and learning:
https://www.stat.cmu.edu/~aramdas/gtpsl/index.html
Papers of interest:
Time-uniform central limit theory and asymptotic confidence sequences: https://arxiv.org/abs/2103.06476
Game-theoretic statistics and safe anytime-valid inference: https://arxiv.org/abs/2210.01948
Discussion papers:
Safe Testing: https://arxiv.org/abs/1906.07801
Testing by Betting: https://academic.oup.com/jrsssa/article/184/2/407/7056412
Estimating means of bounded random variables by betting: https://academic.oup.com/jrsssb/article/86/1/1/7043257
Follow along on Twitter:
The American Journal of Epidemiology: @AmJEpi
Ellie: @EpiEllie
Lucy: @LucyStats
🎶 Our intro/outro music is courtesy of Joseph McDade
Edited by Cameron Bopp -
Cookies, Causal Inference, and Careers with Ingrid Giesinger #Epicookiechallenge
Ingrid is a doctoral student in Epidemiology at the Dalla Lana School of Public Health at the University of Toronto.
Winning cookie recipe
Follow along on Twitter:
The American Journal of Epidemiology: @AmJEpi
Ellie: @EpiEllie
Lucy: @LucyStats
🎶 Our intro/outro music is courtesy of Joseph McDade
Edited by Cameron Bopp -
Analyzing the Analysts: Reproducibility with Nick Huntington-Klein
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: https://theeffectbook.net/
The Paper of How: https://onlinelibrary.wiley.com/share/W2FMEESMMSJMWDEZYY8Y?target=10.1111/obes.12598
Nick’s twitter & BlueSky: @nickchk
Nick’s website: https://nickchk.com
Follow along on Twitter:
The American Journal of Epidemiology: @AmJEpi
Ellie: @EpiEllie
Lucy: @LucyStats
🎶 Our intro/outro music is courtesy of Joseph McDade
Edited by Cameron Bopp