We are pleased to announce The Centennial Workshop on Quantum Causality, Probability, and Information, to be held at Harvard University in Cambridge, Massachusetts, from September 25–27, 2026.
In the year when we celebrate the 100th anniversary of the publication of Schrödinger equation and Born’s rule, this interdisciplinary workshop will bring together leading researchers from physics, statistics, philosophy, and data science to examine foundational questions at the intersection of causality, probability, and information in modern science. Hosted in collaboration with the Harvard Data Science Initiative and Harvard Data Science Review, the meeting aims to foster deep dialogue across disciplines at a time when advances in quantum theory, large-scale data, and artificial intelligence are reshaping scientific inference.
Scope and Vision
Causality and probability lie at the core of scientific reasoning, yet their interpretation and application vary significantly across fields. In quantum physics, probability enters in non-classical settings; in statistics and AI, it underpins inference from data and prediction; in philosophy, it raises enduring conceptual questions about explanation and knowledge.
This workshop seeks to clarify these perspectives and explore their connections, with particular emphasis on the role of information as a unifying theme across disciplines.
Key guiding questions include:
- What does causality mean in modern science, particularly in quantum contexts?
- How should probability be interpreted across physical theory, statistics, and machine learning?
- What is the relationship between causal models, probabilistic inference, and information-theoretic frameworks?
- Can quantum phenomena be understood within broader probabilistic or informational paradigms?
- How do advances in AI reshape our understanding of uncertainty, inference, and scientific explanation?
Topics of Interest
The program will span a range of interconnected themes, including:
- Foundations of causation and causal modeling
- Interpretations of probability (frequentist, Bayesian, quantum, and beyond)
- Quantum probability and its relation to classical probability theory
- Information-theoretic approaches to physics and inference
- The relationship between correlation, causation, and signaling
- Uncertainty quantification in modern physics and data-driven science
- The role of AI and machine learning in scientific reasoning
Expected Outcomes
The workshop aims to generate:
- A curated set of questions identifying key open challenges
- A special issue of the Harvard Data Science Review with workshop’s contributions
Organizers
- Christine Aidala (University of Michigan)
- Jacob Barandes (Harvard University)
- Hanti Lin (University of California, Davis)
- Xiao-Li Meng (Harvard University)
- Pavel Nadolsky (Michigan State University)