最佳Causal Inference播客 (2024)

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Causal Bandits @ CLeaR 2024 | Part 2 | CausalBanditsPodcast.com 22:05

5d ago22:05

22:05

Send us a text Which models work best for causal discovery and double machine learning? In this extra episode, we present 4 more conversations with the researchers presenting their work at the CLeaR 2024 conference in Los Angeles, California. What you'll learn: - Which causal discovery models perform best with their default hyperparameters? - How t…

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Causal Bandits @ CLeaR 2024 | Part 1 | CausalBanditsPodcast.com 22:11

26d ago22:11

22:11

Send us a text Root cause analysis, model explanations, causal discovery. Are we facing a missing benchmark problem? Or not anymore? In this special episode, we travel to Los Angeles to talk with researchers at the forefront of causal research, exploring their projects, key insights, and the challenges they face in their work. Time codes: 0:15 - 02…

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Causal Bandits @ AAAI 2024 | Part 2 | CausalBanditsPodcast.com 22:38

1M ago22:38

22:38

Send us a text *Causal Bandits at AAAI 2024 || Part 2* In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada. Time codes: 00:12 - 04:18 Kevin Xia (Columbia University) - Transportability 4:19 - 9:53 Patrick Altmeyer (Delft) - Explainability & black-box models 9:54 - 12:24 Lokesh Nagalapatti (IIT…

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Causal Bandits @ AAAI 2024 | Part 1 | CausalBanditsPodcast.com 19:35

2M ago19:35

19:35

Send us a text Causal Bandits at AAAI 2024 || Part 1 In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada and participants of our workshop on causality and large language models (LLMs) Time codes: 00:00 Intro 00:20 Osman Ali Mian (CISPA) - Adaptive causal discovery for time series 04:35 Emily M…

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Genome-Wide Association Studies With Dr Rotter 23:26

2M ago23:26

23:26

Jerome I. Rotter, MD, The Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute, Department of Pediatrics, Harbor-UCLA Medical Center, discusses Genome-Wide Association Studies with JAMA Statistical Editor Roger J. Lewis, MD, PhD. Related Content: Genome-Wide Association Studies…

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Free Will, LLMs & Intelligence | Judea Pearl Ep 21 | CausalBanditsPodcast.com 54:54

3M ago54:54

54:54

Send us a text Meet The Godfather of Modern Causal Inference His work has pretty literally changed the course of my life and I am honored and incredibly grateful we could meet for this great conversation in his home in Los Angeles To anybody who knows something about modern causal inference, he needs no introduction. He loves history, philosophy an…

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Conditional Power: How Likely Is Trial Success? 16:28

3M ago16:28

16:28

Benjamin R. Saville, PhD, Department of Biostatistics, Vanderbilt University Medical Center, discusses Conditional Power: How Likely Is Trial Success? with JAMA Statistical Editor Roger J. Lewis, MD, PhD. Related Content: Conditional Power

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Causal AI & Individual Treatment Effects | Scott Mueller Ep. 20 | CausalBanditsPodcast.com 53:28

3M ago53:28

53:28

Send us a text Can we say something about YOUR personal treatment effect? The estimation of individual treatment effects is the Holy Grail of personalized medicine. It's also extremely difficult. Yet, Scott is not discouraged from studying this topic. In fact, he quit a pretty successful business to study it. In a series of papers, Scott describes …

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Starting the Conversation on Models with Alyssa Bilinski | Season 5 Episode 11 48:12

4M ago48:12

48:12

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 note…

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Bayesian Hierarchical Models With Dr McGlothlin 15:07

4M ago15:07

15:07

Anna E. McGlothlin, PhD, Berry Consultants, LLC, discusses Bayesian Hierarchical Models with JAMA Statistical Editor Roger J. Lewis, MD, PhD. Related Content: Bayesian Hierarchical Models

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Causal AI in Personalization | Dima Goldenberg Ep 19 | CausalBanditsPodcast.com 1:07:27

4M ago1:07:27

1:07:27

Send us a text Video version of this episode is available here Causal personalization? Dima did not love computers enough to forget about his passion for understanding people. His work at Booking.com focuses on recommender systems and personalization, and their intersection with AB testing, constrained optimization and causal inference. Dima's pass…

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Flexible methods with Edward Kennedy | Season 5 Episode 10 38:57

4M ago38:57

38:57

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…

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Causal Inference for Drug Repurposing & CausalLib | Ehud Karavani Ep 18 | CausalBanditsPodcast.com 54:19

5M ago54:19

54:19

Send us a text Was Deep Learning Revolution Bad For Causal Inference? Did deep learning revolution slowed down the progress in causal research? Can causality help in finding drug repurposing candidates? What are the main challenges in using causal inference at scale? Ehud Karavani, the author of the CausalLib Python library and Researcher at IBM Re…

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What Sports and Feminism can tell us about Causal Inference with Sheree Bekker & Stephen Mumford | Season 5 Episode 9 49:43

5M ago49:43

49:43

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 …

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Sequential, Multiple Assignment, Randomized Trial Designs With Dr Kidwell 13:27

5M ago13:27

13:27

Kelley Kidwell, PhD, professor of biostatistics, University of Michigan, discusses Sequential, Multiple Assignment, Randomized Trial Designs with JAMA Statistical Editor Roger J. Lewis, MD, PhD. Related Content: Sequential, Multiple Assignment, Randomized Trial Designs

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From Physics to Causal AI & Back | Bernhard Schölkopf Ep 17 | CausalBanditsPodcast.com 35:08

5M ago35:08

35:08

Send us a text Causal AI: The Melting Pot. Can Physics, Math & Biology Help Us? What is the relationship between physics and causal models? What can science of non-human animal behavior teach causal AI researchers? Bernhard Schölkopf's rich background and experience allow him to combine perspectives from computation, physics, mathematics, biology, …

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Observational Causal Analyses with Erick Scott | Season 5 Episode 8 51:43

5M ago51:43

51:43

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…

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Open Source Causal AI & The Generative Revolution | Emre Kıcıman Ep 16 | CausalBanditsPodcast.com 43:08

6M ago43:08

43:08

Send us a text What makes two tech giants collaborate on an open source causal AI package? Emre's adventure with causal inference and causal AI has started before it was trendy. He's one of the original core developers of DoWhy - one of the most popular and powerful Python libraries for causal inference - and a researcher focused on the intersectio…

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Friends Let Friends Do Mediation Analysis with Nima Hejazi | Season 5 Episode 7 59:07

6M ago59:07

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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. Su…

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Why Hinton Was Wrong, Causal AI & Science | Thanos Vlontzos Ep 15 | CausalBanditsPodcast.com 1:06:16

6M ago1:06:16

1:06:16

Send us a text Recorded on Jan 17, 2024 in London, UK. Video version available here What makes so many predictions about the future of AI wrong? And what's possible with the current paradigm? From medical imaging to song recommendations, the association-based paradigm of learning can be helpful, but is not sufficient to answer our most interesting …

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Target Trial Emulation for Causal Inference From Observational Data With Dr Hernán 27:25

6M ago27:25

27:25

Miguel A. Hernán, MD, DrPH, professor of epidemiology, Harvard T.H. Chan School of Public Health, discusses Target Trial Emulation: A Framework for Causal Inference From Observational Data with JAMA Statistical Editor Roger J. Lewis, MD, PhD. Related Content: Target Trial Emulation

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Fun and Game(s) Theory with Aaditya Ramdas | Season 5 Episode 6 48:23

6M ago48:23

48:23

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, cali…

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Cookies, Causal Inference, and Careers with Ingrid Giesinger #Epicookiechallenge | Season 5 Episode 5 46:49

7M ago46:49

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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…

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Causal Inference & Financial Modeling with Alexander Denev Ep 14 | CausalBanditsPodcast.com 1:04:57

7M ago1:04:57

1:04:57

Send us a text Video version available here Are markets efficient, and if not, can causal models help us leverage the inefficiencies? Do we really need to understand what we're modeling? What's the role of symmetry in modeling financial markets? What are the main challenges in applying causal models in finance? Ready to dive in? About The Guest Ale…

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On Deep Learning for Medical Image Analysis With Dr Carin 22:54

7M ago22:54

22:54

JAMA Statistical Editor Roger J. Lewis, MD, PhD, discusses On Deep Learning for Medical Image Analysis with Lawrence Carin, PhD. Related Content: On Deep Learning for Medical Image Analysis

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Analyzing the Analysts: Reproducibility with Nick Huntington-Klein | Season 5 Episode 4 45:44

7M ago45:44

45:44

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 fo…

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Causal Inference & Reinforcement Learning with Andrew Lampinen Ep 13 | CausalBanditsPodcast.com 56:12

7M ago56:12

56:12

Send us a text Love Causal Bandits Podcast? Help us bring more quality content: Support the show Video version of this episode is available here Causal Inference with LLMs and Reinforcement Learning Agents? Do LLMs have a world model? Can they reason causally? What's the connection between LLMs, reinforcement learning, and causality? Andrew Lampine…

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Immortal Time Bias | Season 5 Episode 3 34:37

8M ago34:37

34:37

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/3680…

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Causal Inference, Clinical Trials & Randomization || Stephen Senn || Causal Bandits Ep. 012 (2024) 1:18:52

8M ago1:18:52

1:18:52

Send us a text Support the show Video version available on YouTube Do We Need Probability? Causal inference lies at the very heart of the scientific method. Randomized controlled trials (RCTs; also known as randomized experiemnts or A/B tests) are often called "the golden standard for causal inference". It's a less known fact that randomized trials…

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Patient-Reported Outcome Measures in Clinical Research With Drs Weinfurt and Reeve 22:10

8M ago22:10

22:10

JAMA Statistical Editor Roger J. Lewis, MD, PhD, discusses Patient-Reported Outcome Measures in Clinical Research with Kevin P. Weinfurt, PhD, and Bryce B. Reeve, PhD. Related Content: Patient-Reported Outcome Measures in Clinical Research

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Targeted Learning with Mar van der Laan | Season 5 Episode 2 51:21

8M ago51:21

51:21

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-section…

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Causal Models, Biology, Generative AI & RL || Robert Ness || Causal Bandits Ep. 011 (2024) 1:14:20

8M ago1:14:20

1:14:20

Send us a text Support the show Video version available on YouTube Recorded on Nov 12, 2023 in Undisclosed location, Undisclosed location From Systems Biology to Causality Robert always loved statistics. He went to study systems biology, driven by his desire to model natural systems. His perspective on causal inference encompasses graphical models,…

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Pros and Cons of Randomized Controlled Trials | Season 5 Episode 1 17:55

8M ago17:55

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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 o…

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Causal AI & Supply Chain || Ishansh Gupta || Causal Bandits Ep. 010 (2024) 59:04

9M ago59:04

59:04

Send us a text Support the show Video version available on YouTube Recorded on Sep 27, 2023 in München, Germany From supply chain to large language models and back Ishansh realized the potential of data when he was just 10 years old, during his time as a junior cricket player. His journey led him to ask questions about the mechanisms behind the obs…

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On Causal Inference in Fintech & Being an Author || Matheus Facure || Causal Bandits Ep. 009 (2024) 1:08:41

9M ago1:08:41

1:08:41

Send us a text Support the show Video version of this episode is available on YouTube Recorded on Oct 15, 2023 in São Paulo, Brazil Causal Inference in Fintech? For Brave and True Only From rural Brazil to one of the country’s largest banks, Matheus’ journey could inspire many. Similarly to our previous guest, Iyar Lin, Matheus was interested in po…

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Causal ML, Transparency & Time-Varying Treatments || Iyar Lin || Causal Bandits Ep. 008 (2024) 56:49

9M ago56:49

56:49

Send us a text Support the show Video version available on YouTube Recorded on Sep 13, 2023 in Beit El'Azari, Israel The eternal dance between the data and the model Early in his career, Iyar realized that purely associative models cannot provide him with the answers to the questions he found most interesting. This realization laid the groundwork f…

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[Extra]: Mosquitos, Pascal & Hedge Funds || A Walk with Darko Matovski, PhD (causaLens) in London (2024) 22:38

10M ago22:38

22:38

Send us a text Support the show Video version available on YouTube Recorded on Sep 4, 2023 in London, UK A causal bet Darko's story begins in Eastern Europe, where his early attempts in building a business and the influence of early-stage role models shaped his attitudes and helped him move through challenging and lonely moments in his career. See …

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Causal AI, Justin Bieber & Optimal Experiments || Jakob Zeitler || Causal Bandits Ep. 007 (2024) 1:09:24

10M ago1:09:24

1:09:24

Send us a text Support the show Video version of this episode is available here Recorded on Sep 5, 2023 in Oxford, UK Have you ever wondered if we can answer seemingly unanswerable questions? Jakob's journey into causality started when he was 12 years old. Deeply dissatisfied with what adults had to offer when asked about the sources of causal know…

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Immortal Time Bias in Observational Studies With Dr Kabir Yadav 15:06

10M ago15:06

15:06

JAMA Statistical Editor Roger J. Lewis, MD, PhD, discusses Immortal Time Bias in Observational Studies with Kabir Yadav, MDCM, MS, MSHS. Related Content: Immortal Time Bias in Observational Studies

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