Casual Inference

10 Episodes
Subscribe

By: Lucy D'Agostino McGowan and Ellie Murray

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.

What Sports and Feminism can tell us about Causal Inference with Sheree Bekker & Stephen Mumford
#57
06/12/2024

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...


Observational Causal Analyses with Erick Scott
#56
05/29/2024

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
#55
05/16/2024

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...


Fun and Game(s) Theory with Aaditya Ramdas
#54
05/01/2024

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...


Cookies, Causal Inference, and Careers with Ingrid Giesinger #Epicookiechallenge
#53
04/17/2024

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
#52
04/03/2024

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

Follo...


Immortal Time Bias
#51
03/20/2024

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: https://link.springer.com/article/10.1007/s40471-024-00346-2 

Immortal time in pregnancy: https://pubmed.ncbi.nlm.nih.gov/36805380/ 

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


Targeted Learning with Mar van der Laan
#50
03/06/2024

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: https://ctml.berkeley.edu/

A causal roadmap: https://pubmed.ncbi.nlm.nih.gov/37900353/ 

Short course on causal learning: https://ctml.berkeley.edu/introduction-causal-inference 

Handbook on the TLverse (Targeted Learning in R): https://ctml.berkeley.edu/publications/tar...


Pros and Cons of Randomized Controlled Trials
#49
02/21/2024

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). https://doi.org/10.1186/s12919-023-00285-8

Follow along o...


Remembering Ralph B. D'Agostino, Sr.
#48
10/02/2023

We are re-releasing an episode from 2021 in remembrance of Ralph D'Agostino, Sr. 

Ellie 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 e...