talks

Selected talks and presentations are listed below. The CV contains the complete record.

Invited talks and seminars

• Online Causal Inference Seminar, Young Researchers’ Seminar (online, 2026) — Automatic Debiased Machine Learning for Functionals of M-estimands
• American Causal Inference Conference, invited session on automatic nonparametric (Riesz) learning (Salt Lake City, 2026) — Generalized autoDML for Nonparametric M-estimands
• National Association for Business Economics (Seattle, 2025) — Semiparametric Double Reinforcement Learning with Applications to Long-Term Causal Inference
• International Symposium on Biopharmaceutical Statistics, invited session (Baltimore, 2024) — Adaptive Targeted Machine Learning of the Average Treatment Effect Using the Highly Adaptive Lasso
• Forum on the Integration of Observational and Randomized Data (Washington, DC, 2023) — Adaptive Debiased Machine Learning Using Data-Driven Model Selection Techniques
• International Conference on Computational and Methodological Statistics (Berlin, 2023) — Adaptive Debiased Machine Learning with Model Selection
• Western North American Region of the International Biometric Society (Anchorage, 2023) — Nonparametric Inference on the Causal Effect of a Stochastic Threshold-Based Intervention
• University College London Semiparametric Statistics Reading Group (online, 2026) — Adaptive Debiased Machine Learning Using Data-Driven Model Selection Techniques
• Seminars at Université de Montpellier (2025), the International Seminar on Selective Inference (2024), the Stanford Causal Inference Working Group (2023), and the University of Pennsylvania Causal Inference Seminar (2023)

Invited short courses

• American Causal Inference Conference (Seattle, 2024) — Highly Adaptive Lasso and Adaptive TMLE in Causal Inference, co-taught with Mark van der Laan and Rachael Phillips

Contributed talks

• Joint Statistical Meetings, topic-contributed paper session (Boston, 2026) — Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
• American Causal Inference Conference (Detroit, 2025) — Automatic Double Reinforcement Learning in Semiparametric MDPs
• Conference on Causal Learning and Reasoning (Lausanne, 2025) — Stabilized IPW via Isotonic Calibration (Best Paper)
• MIT Conference on Digital Experimentation (Cambridge, 2024) — Long-Term Causal Inference from Short-Term Experiments Using Reinforcement Learning

Posters

• NeurIPS 2026 — Calibeating Prediction-Powered Inference; Inverse Reinforcement Learning with Just Classification and a Few Regressions
• Stanford Causal Science Center Student Conference 2026 — Grounded Causal Inference
• ICML 2025 — Generalized Venn and Venn-Abers Calibration
• ACIC 2025 — Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands; Automatic Doubly Robust Inference via Calibration
• NeurIPS 2024 — Self-Calibrating Conformal Prediction
• ACIC 2024 — Combining T-learning and DR-learning: A Framework for Oracle-Efficient Estimation of Causal Contrasts
• ICML 2023 — Causal Isotonic Calibration for Heterogeneous Treatment Effects