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] āA causal roadmap for generating high-quality real-world evidenceā https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603361/ Follow along on Twitter: The American Journal of Epidemiology: Ellie: Lucy: š¶ 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: š¶ 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. Heās 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. Nickās book, online version: The Paper of How: Nickās twitter & BlueSky: @nickchk Nickās 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: š¶ 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...
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Pros and Cons of Randomized Controlled Trials | Season 5 Episode 1
Casual Inference
Ellie and Lucy kick off the season and introduce our new executive buzzer, Melita! Melita is a masters student in statistics at Wake Forest University and will be helping out with the podcast (and keeping Lucy and Ellie from using too much jargon!) Pros & Cons of RCT paper: Fernainy, P., Cohen, A.A., Murray, E. et al. Rethinking the pros and cons of randomized controlled trials and observational studies in the era of big data and advanced methods: a panel discussion. BMC Proc 18 (Suppl 2), 1 (2024). Follow along on Twitter: The American Journal of Epidemiology: Ellie: ...
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Remembering Ralph B. D'Agostino, Sr.
Casual Inference
We are re-releasing an episode from 2021 in remembrance of . Ellie Murray and Lucy DāAgostino McGowan chat with Ralph DāAgostino Sr. and Ralph DāAgostino 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 DāAgostino 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...
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Evidence Science with Cat Hicks | Season 4 Episode 11
Casual Inference
Ellie and Lucy chat with Dr. Cat Hicks, VP of Research Insights and at Pluralsight Flow, about evidence science. Follow along on Twitter: Cat: The American Journal of Epidemiology: Ellie: Lucy: š¶ Our intro/outro music is courtesy of Edited by Quinn Rose:
info_outlineIn this episode Lucy D'Agostino McGowan and Ellie Murray chat with Sander Greenland, Emeritus Professor of Epidemiology and Statistics at UCLA.
Follow along on Twitter:
- The American Journal of Epidemiology: @AmJEpi
- Ellie: @EpiEllie
- Lucy: @LucyStats
š¶ Our intro/outro music is courtesy of Joseph McDade
Edited by Quinn Rose: aspiringrobot.com