talks

upcoming

• Workshop on Translational Research on Data Heterogeneity, April 6, 2024

past

• MRC Biostat seminar, Nov 14, 2023
• Statistics Seminar, UGent, Nov 16, 2023
• IMS Young Mathematical Scientist Forum, National University of Singapore, Nov 20-23, 2023
• Statistics Seminar, Technical University of Munich, Dec 6, 2023
• Online causal inference seminar, Oct 31, 2023
• Two factorizations and a density ratio, on `Parameterizing and simulating from causal models’ by Evans and Didelez. Discussion meeting, Royal Statistical Society, London, Oct 2023. [Slides]
• Pacific Causal Inference Conference 2023. Sept 16-17, 2023.
• Latent variable justifies the stronger IV bounds. Causal Inference & Quantum Foundations Workshop, Perimeter Institute, April 2023.
• Harnessing Extra Randomness: Reproducibility, Flexibility and Causality.
  • Statistics, EPFL, Oct 2023
  • ACMS, University of Notre Dame, Jan 2023
  • Statistics, UC Riverside, Jan 2023
  • Data Sciences and Operations, Marshall School, USC, Jan 2023
  • Statistics, University of Michigan, Jan 2023
  • Statistics & Operations Research, UNC, Jan 2023
  • Statistics, Iowa State, Jan 2023
  • Biostatistics, University of Washington, Jan 2023
  • Statistics, University of Connecticut, Feb 2023
  • Statistics, University of Pittsburgh, Feb 2023
• Inference for multiple data splitting and exchangeable p-values. CMStatistics 2022, King’s College London, Dec 17, 2022.
• Variable elimination, graph reduction and efficient g-formula. Fellows Seminar, Simons Institute, UC Berkeley, May 2022.
• Latent variable justifies the stronger IV bounds. Quantum Physics and Statistical Causal Models Workshop, Simons Institute, UC Berkeley, April 2022.
• Variable elimination, graph reduction and efficient g-formula. Causal Inference Reading Group, UC Berkeley, Feb 23, 2022.
• Variable elimination, graph reduction and efficient g-formula. Young Data Science Researcher Seminar, ETH Zurich, Feb 17, 2022.
• Variable elimination, graph reduction and efficient g-formula. Biostatistics, UC Berkeley, Feb 7, 2022.
• Variable elimination and graph reduction: towards an efficient g-formula. CMStatistics 2021, King’s College London, Dec 2021.
• Automated analysis of discrete IV with finite-sample guarantees. The Future of Mendelian Randomization Studies, Lorentz Center (online), Dec 2021.
• Variable elimination and graph reduction: towards an efficient g-formula for causal DAGs. Statistics Seminar, Statistical Laboratory, Cambridge, Nov 2021.
• Efficient least squares for estimating total causal effects. Pacific Causal Inference Conference, Sept 2020.
• Possible causal effects: enumeration and estimation. UW Statistics Seminar, May 22, 2020.
• Empirical Bayes for A/B testing and meta-analysis: a spectral approach. UW CSSS Seminar, May 6, 2020.
• On testing marginal versus conditional independence. UW Causal Working Group, Oct 2019.