With PhD students, postdocs, and associated researchers, we organize weekly seminars - currently only online. In this weekly group meeting, we alternate presentations by invited researchers on the topics of missing data, causal inference but also ML/statistics in general, with presentations by students of the group and also paper reading sessions.

Here you can find a list of group meetings with abstracts. We regularly update the links and list of confirmed speakers. Please contact Pan Zhao if you are interested to get the slides or if you want to visit us or to receive the announcement for the talks.

See below the calendar for more details.

Group Meeting

  • 2023-09-25, Waverly (Linqing) Wei, PhD in Biostatistics at University of California, Berkeley, USA: Adaptive Experiments Toward Learning Treatment Effect Heterogeneity.

  • 2023-09-11, Badr-Eddine Chérief-Abdellatif, CNRS researcher (Chargé de Recherche) at Sorbonne Université & Université Paris Cité, France: Label Shift Quantification via Distribution Feature Matching.

  • 2023-05-31, Georgia Tomova, PhD student at University of Leeds, UK: Distinguishing the transparency, explainability, and interpretability of algorithms.

  • 2023-05-22, Ying Jin, PhD student at Stanford University, USA: Selection by Prediction: Screening and Discovery with (Weighted) Conformal p-values.

  • 2023-05-16, Zijian Guo, Associate Professor at Rutgers University, USA: Statistical Inference for Maximin Effects: Identifying Stable Associations Across Multiple Studies.

  • 2023-04-25, Judith Abécassis, Junior PI (ISFP) at Inria Saclay, France: Exploring cognition in the UK Biobank with mediation analysis.

  • 2023-04-24, Alexis Ayme, PhD student at Sorbonne University, France: Linear prediction with NA, imputation versus specific methods.

  • 2023-04-20, Houssam Zenati, PhD student at Inria, France: Sequential Counterfactual Risk Minimization.

  • 2023-04-12, Jeffrey Näf, Postdoc at Inria, France: Distributional Random Forest: Heterogeneity Adjustment and Multivariate Distributional Regression.

  • 2023-04-12, Hugo Senetaire, PhD student at Inria, France: Explainability as statistical inference.

  • 2023-04-05, Joseph Antonelli, Assistant Professor at the University of Florida, USA: Heterogeneous causal effects of neighborhood policing in New York City with staggered adoption of the policy.

  • 2023-03-29, Gaël Varoquaux, Research director at Inria, France: Machine learning for health and society? Progress and vision.

  • 2023-03-20, Mike Van Ness, PhD student at Stanford University, USA: The Missing Indicator Method: From Low to High Dimensions.

  • 2023-03-13, Pan Zhao, PhD student at Inria, France: Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data.

  • 2023-03-06, Margaux Zaffran, PhD student at Inria, France: Conformal Prediction with Missing Values.

  • 2023-02-27, Lola Etievant, Postdoctoral fellow at National Cancer Institute, USA: Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data.

  • 2023-02-22, Guillaume Martin, MD and PhD student at Sorbonne Université, France: Meta-analyses, heterogeneity, and meta-epidemiology.

  • 2023-02-13, Eric Dunipace, MD student at David Geffen School of Medicine at UCLA, USA: Optimal transport weights for causal inference.

  • 2023-01-18, Kat Hoffman, Senior Data Analyst at Columbia University Medical Center, USA: Comparison of a Target Trial Emulation Framework vs Model-First Approaches to Estimate the Effect of Corticosteroids on COVID-19 Mortality.

  • 2023-01-09, Daisy Ding, PhD student at Stanford University, USA: Cooperative learning for multiview analysis.

  • 2023-01-04, Linus Bleistein, PhD student at Inria, France: Learning the dynamics of sparsely observed interacting systems.

  • 2022-11-30, Xiao Wu, Postdoc at Stanford University, USA: Assessing the causal effects of a stochastic intervention in time series data.

  • 2022-11-29, Yiye Jiang, PhD student at Institut de Mathématiques de Bordeaux, France: Graph learning with autoregressive models for complex data types.

  • 2022-11-21, Aude Sportisse, Postdoc at 3iA Côte d’Azur, France: Informative labels in Semi-Supervised Learning.

  • 2022-11-16, Marco Carone, Associate Professor of Biostatistics at University of Washington, USA: Inference for algorithm-agnostic variable importance.

  • 2022-11-02, Curtis Northcutt, CEO & Co-Founder of Cleanlab, USA: Cleanlab: Automatically Find and Fix Errors in ML Datasets.

  • 2022-10-26, Ruishan Liu, Postdoc at Stanford, USA: AI for clinical trials design.

  • 2022-10-18, Arthur Gretton, Professor at University College London, UK: Deep Causal Inference.

  • 2022-10-14, Matthieu Marbac-Lourdelle, Assistant Professor at ENSAI/CREST, France: Model selection for non-parametric mixtures and hidden Markov models.

  • 2022-09-27, Elizabeth Stuart, Professor at Johns Hopkins University, USA: Combining experimental and population data to estimate population treatment effects.

  • 2022-09-14, Mary Beth Nebel, Assistant Professor of Neurology at Johns Hopkins University, USA: Accounting for motion in fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

  • 2022-07-06, Romain Pirracchio, Professor at University of California San Francisco, USA: Heterogeneous Treatment Effect in medicine: convergence between ML and Causal Inference.

  • 2022-06-29, François Husson, Professor at Agrocampus Ouest, France: Visualisation de données multi-tableaux par analyse factorielle multiple.

  • 2022-06-15, Alexandre Perez, PhD Student at SoDa team, Inria, France: Beyond calibration: estimating the grouping loss of modern neural networks.

  • 2022-06-10, Eric Laber, Professor of Statistical Science at Duke University, USA: Safe Contextual Bandits in mHealth.

  • 2022-06-07, Sofia Triantafyllou, Assistant Professor at University of Crete, Greece: Causal effect estimation using observational and experimental data.

  • 2022-06-03, Michael Elliott, Professor of Biostatistics at the University of Michigan, USA: Using Synergies Between Survey Statistics and Causal Inference to Improve Transportability of Clinical Trials.

  • 2022-05-30, Sylvain Sardy, Associate Professor at University of Geneva, Switzerland: A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks.

  • 2022-05-18, Chris Harschaw, Postdoctoral Scholar at Simons Institute, USA: Interference in Randomized Experiments: Survey and Challenges.

  • 2022-05-16, Jeffrey Näf and Meta Lina Spohn, PhD students at ETH Zürich, Switzerland: Imputation Scores: How to choose an imputation method?

  • 2022-05-11, Erica Moodie, Professor of Biostatistics at McGill University, Canada: Regression-Based Methods To Estimate Adaptive Treatment Strategies.

  • 2022-04-27, Nathan Kallus, Assistant Professor at Cornell University and Cornell Tech, USA: Smooth Contextual Bandits.

  • 2022-03-30, Felipe Tobar, Associate Professor at the Universidad de Chile, Chile: Gaussian processes, missing data, and optimal transport.

  • 2022-03-14, Jiwei Zhao, Assistant Professor at the University of Wisconsin–Madison, USA: A Journey of Understanding Nonignorable Missingness and Some Reflections.

  • 2022-02-16, Fredrik Johansson, Assistant Professor at Chalmers University of Technology, Sweden: Generalization Bounds for Estimation of Causal Effects.

  • 2022-02-14, Celestine Mendler-Dünner, Research Group Lead at the Max Planck Institute for Intelligent Systems in Tübingen, Germany: Performative Prediction.

  • 2022-01-26, Torsten Hothorn, Professor at the University of Zürich, Switzerland: Transformation Models: Pushing the Boundaries.

  • 2022-01-17, Robin Genuer, Associate professor at ISPED, Université de Bordeaux, France: Fréchet random forests for metric space valued regression with non euclidean predictors.

  • 2022-01-12, David Haziza, Professor at the University of Ottawa, Canada: Efficient multiply robust imputation in the presence of influential units in surveys.

  • 2022-01-10, Ioanna Manolopoulou, Associate Professor at University College London, UK: Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation from randomized and observational data.

  • 2022-01-05, Clément Bénard, Research Engineer in Machine Learning & Statistics at Safran, France: Variable importance for random forests: MDA and Shapley effects.


Reading Groups

  • Introduction to causal inference for statisticians

  • Targeted learning


We also organize common group meetings with Gaël Varoquaux’s Inria team. For more information about these group meetings, contact Bénédicte Colnet.

Members of our group follow different seminars and events, a selection is given in the google calendar. Additionally, we keep a list of conferences and summer schools that some of us potentially plan to attend here.