Interactive Causal
Learning conference

Reality and Our Ability to Explain It

James Madden (Benedictine College)

Going Behind the Back of Consciousness: The Pre-Cognitive Contact with Reality and Learning to Operate in the Space of Reasons

Fabrizio Cariani (University of Maryland)

Causality’s Shadow in Language

From Causal Insights to Deeper Learning

Marco Lin (University College London)

Active Inference, Curiosity, and Insight

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

Mark van der Laan (UC Berkeley)

Higher Order Targeted Maximum Likelihood Estimation

Vasilis Syrgkanis (Microsoft)

Automatic Debiased Machine Learning with Generic Machine Learning for Static and Dynamic Causal Parameters

Lihua Lei (Stanford University)

Conformal Inference of Counterfactuals and Individual Treatment Effects

Learning in Socio-Economic Settings

Burkhard Schipper (UC Davis)

Predicting the Unpredictable under Subjective Expected Utility

Albin Erlanson (University of Essex)

Optimal Allocations with Capacity Constrained Verification

Nicola Gatti (Politecnico di Milano)

Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints

Grasping Consequences; From Individual to Social

Jeremy Wilkins (Boston College)

Who’s Asking? Insight and the Problem of Getting Questions Right

Brian Epstein (Tufts University)

Causation and the Nature of the Social World

Sarah Paul (NYU Abu Dhabi)

Learning What We Can Do

Machine Learning and Estimation 2

Carlos Cinelli (University of Washington)

Omitted Variable Bias in Machine Learned Causal Models

Judith Lok (Boston University)

Causal Organic Indirect and Direct Effects: Closer to Baron and Kenny, and Related to Surrogate Outcomes

Rosemary Ke (DeepMind)

From What to Why: Towards Causal Deep Learning

Causal Discovery and Identification

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)

Causality-Inspired ML: How Can Causality Help ML?

Thomas Icard (Stanford University)

Learning from Causal Explanations