Casual Inference

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:

The American Journal of Epidemiology: @AmJEpi

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 McDade
Edited 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

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 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...

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...

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

Follo...

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/tar...