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Causal AI Workshop
Information and Announcements
Essential Workshop Information and Links
A Causal Inference Workflow
Forming the Inference Question
Building the Causal DAG
Identification: Can Your Data Answer Your Question?
Trying Different Estimators
Refutation: Testing Your Assumptions
Taking a Bayesian and Probabilistic Machine Learning Approach
Causality and Directed Graphical Models
Building a Causal Model as a Directed Graph (DAG) (1:16)
A Computer Science Perspective on DAGs (4:24)
Reasoning about Probability with the DAG (1:46)
Training a Causal Generative Model on a DAG (2:29)
Assessment: Modeling with DAGs
The Causal Markov Property (3:12)
Causal Markov Kernels and Independence of Mechanism (4:32)
Building and Testing your DAG with Data
D-Separation and Colliders (6:25)
Using D-Separation to Describe Conditional Independence (4:42)
Assessment - Validating the Markov Property
Causal Sufficiency: How Big Should My Model Be? (2:01)
Markov Equivalence (2:24)
Partially Directed Acyclic Graphs and Markov Equivalence (1:51)
Coding Equivalence and PDAGs (1:56)
Dealing with Latent Variables
Causal Faithfulness (1:06)
Causal Discovery 101: How (Not) to Learn Graphs from Data
Theory of Interventions
Introduction to Interventions (3:33)
Modeling Interventions with a DAG
Do-operators: Simulating Interventions in a Generative Model
Assessment - Interventions via Graph Surgery
Causal Effects and Interventions (6:44)
Practical Guidelines for Intervention Modeling
Reasoning about Causal Effect Inference
Identifiability for Causal Queries (2:29)
The Do-Calculus and Using Graphs for Identification (2:07)
A Closer Look at the Do-Calculus
Backdoor Adjustment (4:57)
Front-door Adjustment (1:50)
Assessment - Do-Calculus and Backdoor Adjustment
Causal Effect Estimation with DoWhy
Backdoor Adjustment with Linear Regression
Backdoor Adjustment with Propensity Scores (3:58)
Machine Learning Methods for Backdoor Adjustment
Front-Door Adjustment with Linear Regression
Instrumental Variables and Regression Discontinuity (10:47)
Refuting Assumptions in your Causal Inference Workflow
Potential Outcomes Framework and Assumptions (8:11)
Assessment - Statistical Methods for Causal Inference
Combining Probabilistic Deep Learning and Causal Graphical Models
Programming Probabilistic Generative Models of Causality
Training a Causal Image Model with Variational Inference
Using Causality to Enhance Deep Learning; Semi-Supervised Learning Case Study (7:16)
Bayesian Graphical Causal Inference with MCMC (10:15)
Deep Bayesian Graphical Causal Inference with Variational Inference
Using Large Language Models to Support Causal Inference
Building a Causal Large Language Model
Wrapping Up
You did it! Next steps.
Identification: Can Your Data Answer Your Question?
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