presentations
Presentation recordings and slides are available through the links below
Reality and Our Ability to Explain It |
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Fabrizio Cariani (University of Maryland) |
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From Causal Insights to Deeper Learning |
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Marco Lin (University College London) |
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Konrad Kording (University of Pennsylvania) |
Active Inference, Curiosity, and Causal Learning in Neuroscience |
Michel Besserve (Max Planck Institute) |
Learning to Intervene in Complex Systems, From Neural Networks to Sustainable Economies |
Machine Learning and Estimation 1 |
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Mark van der Laan (UC Berkeley) |
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Vasilis Syrgkanis (Microsoft) |
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Lihua Lei (Stanford University) |
Conformal Inference of Counterfactuals and Individual Treatment Effects |
Learning in Socio-Economic Settings |
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Burkhard Schipper (UC Davis) |
Predicting the Unpredictable under Subjective Expected Utility |
Albin Erlanson (University of Essex) |
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Nicola Gatti (Politecnico di Milano) |
Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints |
Grasping Consequences; From Individual to Social |
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Jeremy Wilkins (Boston College) |
Who’s Asking? Insight and the Problem of Getting Questions Right |
Brian Epstein (Tufts University) |
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Sarah Paul (NYU Abu Dhabi) |
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Machine Learning and Estimation 2 |
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Carlos Cinelli (University of Washington) |
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Judith Lok (Boston University) |
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Rosemary Ke (DeepMind) |
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Causal Discovery and Identification |
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Emilija Perkovic (University of Washington) |
Total Causal Effect in MPDAGs: Identification and Minimal Enumeration |
Chandler Squires (MIT) |
The Query Complexity of Verifying a Causal Graph from Single-Node Interventions |
Peter Spirtes (Carnegie Mellon University) |
Machine Learning and Learning Causality from Observational Data |
Sara Magliacane (University of Amsterdam) |
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Thomas Icard (Stanford University) |