54 episodes

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.

Casual Inference Lucy D'Agostino McGowan and Ellie Murray

    • Science
    • 4.7 • 96 Ratings

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.

    Cookies, Causal Inference, and Careers with Ingrid Giesinger #Epicookiechallenge

    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

    • 46 min
    Analyzing the Analysts: Reproducibility with Nick Huntington-Klein

    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
     
     

    • 45 min
    Immortal Time Bias

    Immortal Time Bias

    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

    • 34 min
    Targeted Learning with Mar van der Laan

    Targeted Learning with Mar van der Laan

    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/targeted-learning-handbook-causal-machine-learning-and-inference-tlverse-r-software 
    Mark on twitter: @mark_vdlaan 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

    • 51 min
    Pros and Cons of Randomized Controlled Trials

    Pros and Cons of Randomized Controlled Trials

    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 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

    • 17 min
    Remembering Ralph B. D'Agostino, Sr.

    Remembering Ralph B. D'Agostino, Sr.

    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 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. 

    • 49 min

Customer Reviews

4.7 out of 5
96 Ratings

96 Ratings

breggurns ,

Excellent podcast!

I would highly recommend this podcast to anyone interested in Epi/Biostats! Excellent job, this is quickly becoming one of my favorite listens while driving to work!

NickiMina ,

Great conceptually but vocal fry undermines experience as a listener

Highly interested in the episodes and guests but unfortunately find it grating to listen to. Many people do not have natural speaking voices that are well suited for podcasts or radio (myself included). That said, news anchors are a prime example of our ability to improve our speaking voices. The hosts are clearly bright and have great energy, but could benefit from investing in vocal training in the interest of expanding their audience.

Pete Amer ,

Great podcast

This is a really fun and informative podcast on causal inference and data science. The hosts both are great at communicating topics in research design and stats to semi-laypeople like myself.
My only critique is that the audio is pretty quiet, i hope the mic setup can improve.

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