Jonathan gave a guest lecture on causality.
Abstract: We trace the development of causality from a nebulous concept to a precise mathematical theory with significant applications in statistics, AI, econometrics, philosophy, and the biological, health and social sciences. I will present the historical development, and illustrate the key concepts in the theory of causation.
Speaker webpage: https://jonathan-z.github.io/
Slides are available here. References and further reading
- Causality: Models, Reasoning, and Inference (Pearl)
- The Art and Science of Cause and Effect (Pearl)
- Elements of Causal Inference: Foundations and Learning Algorithms (Peters, Janzing, and Schölkopf)
- Mostly Harmless Econometrics (Angrist and Pischke)
- Short Course on Causal Inference (Shakkottai)
- Probabilistic Machine Learning: Advanced Topics (Murphy)
- Probabilistic Graphical Models (Koller and Friedman)
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Stephen talked about using the indicator calculus and mixed-integer linear programming to automatically verify set inequalities.
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