x

75% of people globally want unbiased news (Pew Research) but are concerned they're not getting that.

Get balanced coverage with Symby. See how stories are covered across diverse outlets and spot when one side covers a story the other skips.

PODCAST

Casual Inference

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.

science mathematics

All Episodes

52:55

Optimizing Data Workflows with Emily Riederer |...

Casual Inference ·

2025/06/26

Emily Riederer is a Data Science Senior Manager at Credit Risk Modeling Capital One. Her website can be found here: https://www.emilyriederer.com/ Follow along on Bluesky: Emily: ‪@emilyriederer.bsky.social‬ Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social 🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.

en-us

52:05

Combining Data & Making Effects Generalizable with...

Casual Inference ·

2025/06/12

Carly Brantner is an assistant professor of Biostatistics & Bioinformatics at Duke University and Duke Clinical Research Institute. Follow along on Bluesky: Carly: @carlybrantner.bsky.social Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social 🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.

en-us

49:31

The Art of Clarity with Andrew Heiss | Season 6...

Casual Inference ·

2025/05/29

Andrew Heiss is an assistant professor in the Department of Public Management and Policy at the Andrew Young School of Policy Studies at Georgia State University. Vincent’s “What is your estimand” section in his {marginaleffects} book: https://marginaleffects.com/chapters/challenge.html#sec-goals_estimand Article on defining estimands: https://doi.org/10.1177/00031224211004187 Andrew's marginal effects post: https://www.andrewheiss.com/blog/2022/05/20/marginalia/ Andrew's post on “fixed

en-us

55:22

Study Critique: What Went Wrong and How We'd Do It...

Casual Inference ·

2025/05/08

In this episode Lucy and Ellie dig into a recently publicized paper, "Vaccination and Neurodevelopmental Disorders: A Study of Nine-Year-Old Children Enrolled in Medicaid", which has gained attention after being promoted by RFK Jr. as evidence that vaccines cause autism. Ellie breaks down her Substack critique of the study. Together, she and Lucy discuss the methodological flaws and what a better version of this study might look like. Vaccination and Neurodevelopmental Disorders: A Study of

en-us

45:20

From Model to Meaning with Vincent Arel-Bundock |...

Casual Inference ·

2025/04/24

Vincent Arel-Bundock is a professor at the Université de Montréal, where he studies comparative and international political economy. Vincent's website: https://arelbundock.com/ Vincent's book "Model to Meaning: How to Interpret Statistical Models With marginaleffects for R and Python": https://marginaleffects.com/ Follow along on Bluesky: Vincent: @vincentab.bsky.social Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social 🎶 Our intro/outro music is courtesy of Joseph

en-us

01:22:09

Propensity Scores, R Packages, and Practical Advice...

Casual Inference ·

2025/04/10

Noah Greifer is a statistical consultant and programmer at Harvard University. Episode notes: WeightIt package: https://ngreifer.github.io/WeightIt/ MatchIt package: https://kosukeimai.github.io/MatchIt/ Noah's awesome Stack Exchange post: https://stats.stackexchange.com/a/544958 Follow along on Bluesky: Noah: @noahgreifer.bsky.social Ellie: @EpiEllie.bsky.social Lucy: @LucyStats.bsky.social 🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.

en-us

51:51

Causal Assumptions and Large Language Models | Season...

Casual Inference ·

2025/03/27

Lucy and Ellie chat about large language models, chat interfaces, and causal inference. Do LLMs Act as Repositories of Causal Knowledge?: https://arxiv.org/html/2412.10635v1 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.

en-us

44:48

Data Integration for Impact with Len Testa | Season 6...

Casual Inference ·

2025/02/28

Lucy chats with Len Testa about a recent analysis he did which combined over 150 publicly available data sources to answer a question about the affordability of Disney World. Len's Deep Dive Post on the Touring Plans Blog [Blog Post] Wall Street Journal Artcile, "Even Disney Is Worried About the High Cost of a Disney Vacation" [Article] Follow along on Bluesky: Len: @lentesta.bsky.social Ellie: @EpiEllie.bsky.social Lucy: @LucyStats.bsky.social 🎶 Our intro/outro music is courtesy

en-us

48:12

Starting the Conversation on Models with Alyssa...

Casual Inference ·

2024/07/10

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: https://www.pnas.org/doi/full/10.1073/pnas.2302528120 Shuo Feng’s pre-print:

en-us

38:57

Flexible methods with Edward Kennedy | Season 5...

Casual Inference ·

2024/06/26

Edward Kennedy Associate Professor, Department of Statistics & Data Science, Carnegie Mellon. Episode notes: ehkennedy.com Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California: https://academic.oup.com/aje/article/193/4/673/7425624 Doubly Robust Capture-Recapture Methods for Estimating Population Size: https://www.tandfonline.com/doi/full/10.1080/01621459.2023.2187814 Follow along on Twitter: The

en-us

49:43

What Sports and Feminism can tell us about Causal...

Casual Inference ·

2024/06/12

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,

en-us

51:43

Observational Causal Analyses with Erick Scott |...

Casual Inference ·

2024/05/29

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 McDadeEdited by Cameron Bopp

en-us

59:07

Friends Let Friends Do Mediation Analysis with Nima...

Casual Inference ·

2024/05/16

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

en-us

48:23

Fun and Game(s) Theory with Aaditya Ramdas | Season 5...

Casual Inference ·

2024/05/01

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

en-us

46:49

Cookies, Causal Inference, and Careers with Ingrid...

Casual Inference ·

2024/04/17

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 McDadeEdited by Cameron Bopp

en-us

45:44

Analyzing the analysts: reproducibility with Nick...

Casual Inference ·

2024/04/03

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:

en-us

34:37

Immortal Time Bias | Season 5 Episode 3

Casual Inference ·

2024/03/20

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

en-us

51:21

Targeted Learning with Mar van der Laan | Season 5...

Casual Inference ·

2024/03/06

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

en-us

17:55

Pros and Cons of Randomized Controlled Trials |...

Casual Inference ·

2024/02/21

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

en-us

49:37

Remembering Ralph B. D'Agostino, Sr.

Casual Inference ·

2023/10/02

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

en-us

68 results

Similar Podcasts

X Minus 1 - Old Time Radio Show

Inception Point Ai

Science, Spoken

WIRED

This Week in Space (Audio)

TWiT

DarshanTalks Podcast

Darshan Kulkarni

The Best of Coast to Coast AM

iHeartPodcasts and Coast to Coast AM