Casual Inference Lucy D'Agostino McGowan and Ellie Murray

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
 
 

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

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