Casual Inference

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.

Carly Brantner is an assistant professor of Biostatistics & Bioinformatics at Duke University and Duke Clinical Research Institute. Resources from this episode: multicate: R package for estimating conditional average treatment effects across one or more studies using machine learning methods PCORnet® Front Door: Access point for potential investigators, patient groups, and other stakeholders to connect with PCORnet and get support for potential research studies Patient-Centered Outcomes Data Repository (PDOCR): De-identified data from 24 (and counting) PCORI-funded studies 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.

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 effects” and mariginal effects across different disciplines: https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/ Follow along on Bluesky: Andrew: @andrew.heiss.phd Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social   🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.

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 Nine-Year-Old Children Enrolled in Medicaid: https://publichealthpolicyjournal.com/vaccination-and-neurodevelopmental-disorders-a-study-of-nine-year-old-children-enrolled-in-medicaid/ RFK Jr is promoting a new study claiming "vaccines cause autism" but it doesn't add up. Literally [Ellie's substack]: https://epiellie.substack.com/p/rfk-jr-is-promoting-a-new-study-claiming   Follow along on Bluesky: Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social   🎶 Our intro/outro music is courtesy of Joseph McDade. Edited by Cameron Bopp.

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 McDade. Edited by Cameron Bopp.

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.      

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.

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 of Joseph McDade

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: https://www.medrxiv.org/content/10.1101/2024.04.08.24305335v1 Our uncertainty paper: https://pubmed.ncbi.nlm.nih.gov/33475686/ Follow along on Twitter: Alyssa: @ambilinski The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDadeEdited by Cameron Bopp

Edward Kennedy Associate Professor, Department of Statistics & Data Science, Carnegie Mellon. 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 American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDadeEdited by Cameron Bopp

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). 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 McDadeEdited by Cameron Bopp

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

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 McDadeEdited by Cameron Bopp

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 McDadeEdited by Cameron Bopp

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

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 McDadeEdited by Cameron Bopp    

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 McDadeEdited by Cameron Bopp

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 McDadeEdited by Cameron Bopp

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 McDadeEdited by Cameron Bopp

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.