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Causal Generative Machine Learning Minicourse
Introduction
What You'll Get from this Minicourse
What You'll Learn
Causality, Probability, Graphs, and Interventions
Building a Causal Model as a Directed Acylic Graph (1:16)
Reasoning about Probability with the DAG (1:46)
Training Causal Probability Distributions on a DAG (2:29)
Causal Markov Kernels and Parameter Modularity (4:32)
Modeling Interventions in a Causal Graph
Deep Causal Probabilistic Machine Learning
Models as Symbolic Explanation Generators for Data
Deep Causal Generative Models
Causal Modeling with a Variational Autoencoder
Causal Considerations of Discriminative vs. Generative ML (7:16)
Programming Probabilistic Causal Models
From Graphical Models to Probabilistic Reasoning Systems (8:20)
Probabilistic Programming Defined (10:19)
Programming Causality (6:26)
Applications of Causal Probabilistic Programming
Conclusion
What We Learned
Next Steps
Building a Causal Model as a Directed Acylic Graph
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