Fun and Game(s) Theory with Aaditya Ramdas | Season 5 Episode 6

Casual Inference

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