Casual Inference

Erick Scott is founder of cStructure, a causal science startup. Erick has expertise in medicine, public health, and computational biology.

• [email protected]

• "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

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 McDade
Edited 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 McDade
Edited 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 McDade
Edited 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 McDade
Edited by Cameron Bopp

• 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

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

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

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. 

Ellie and Lucy chat with Dr. Cat Hicks, VP of Research Insights and Director of Developer Success Lab at Pluralsight Flow, about evidence science. 

Follow along on Twitter:

• Cat: @grimalkina
• The American Journal of Epidemiology: @AmJEpi
• Ellie: @EpiEllie
• Lucy: @LucyStats

🎶 Our intro/outro music is courtesy of Joseph McDade
Edited by Quinn Rose: aspiringrobot.com