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
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The American Journal of Epidemiology: @AmJEpi
Ellie: @EpiEllie
Lucy: @LucyStats
🎶 Our intro/outro music is courtesy of Joseph McDade
Edited by Cameron Bopp
Casual Inference Live from SER | Episode 14
Community Engagement, Health Disparities, and Measure Development with Melody Goodman | Episode 13
COVID-19, Masks, and Designing Observational Studies | Episode 12
Getting Bayesian with Frank Harrell | Episode 11
Coronavirus Conversations 2 | Episode 10
Coronavirus Conversations | Episode 09
Causal inference for data science with Sean Taylor | Episode 08
Asking harder causal questions with Whitney Robinson | Episode 07
Internal and External Validity with Elizabeth Stuart | Episode 06
Science Communication with Gideon Meyerowitz-Katz | Episode 05
Quantitative Bias Analysis with Matt Fox | Episode 04
Fairness in Machine Learning with Sherri Rose | Episode 03
Socializing about Social Epidemiology with Onyebuchi Arah | Episode 02
Talking Target Trials with Miguel Hernan | Episode 01
Keeping it casual: the pilot | Episode 00
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