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Causal Modeling in Machine Learning Workshop
Information and Announcements
Essential Workshop Information and Links
Causality and Probabilistic Graphical Models
Building a Causal Model as a Directed Graph (1:16)
Probability and the DAG (1:46)
Training Causal Probability Distributions on a DAG (2:29)
Causal Markov Kernels and Parameter Modularity (4:32)
Generation and Inference (1:43)
Graph Structure and Conditional Independence
A Computer Science Perspective on DAGs (4:24)
D-Separation (2:55)
Key Graphical Concepts in D-Separation: V-Structures and Markov Blankets
Using D-Separation to Describe Conditional Independence (4:42)
Properties and Assumptions of Causal Models
The Causal Markov Property (3:12)
Faithfulness and Minimality (1:06)
Markov Equivalence (2:24)
Partially Directed Acyclic Graphs and Markov Equivalence (1:51)
Coding Equivalence and PDAGs (1:56)
Causal Sufficiency: How Big Should My Model Be? (2:01)
Latent Variables and Ancestral Graphs
Blending Causal Reasoning and Generative Machine Learning
Causal Creation Myths for Data (3:15)
All Myths are Wrong, Some are Useful (3:15)
Deep Generative Causal Models
Causal Modeling with Variational Autoencoders
Causal Considerations of Discriminative vs. Generative ML (7:16)
Theory of Interventions
Interventions vs Observations (3:35)
Modeling Interventions with a DAG
Structural Interventions and Mechanism
Randomization is an Intervention
Interventional Sufficiency, Karl Popper, and Falsifiability
Causal Discovery 101: How (Not) to Learn Graphs from Data
Programming Probabilistic Causal Models
From Bayesian Networks to Probabilistic Reasoning Systems (8:20)
Probabilistic Programming Defined (10:19)
Execution, Sampling, and Conditioning (16:39)
PPL Landscape and Deep Probabilistic Programming
Programming Causality (6:26)
Motivating Examples of Causal Probabilistic Programming
Structural Causal Models
Structural Causal Models as Generative Models (3:22)
Asimov and Laplace Explain SCMs (3:52)
Programming SCMs (2:20)
Interventions on SCMs (1:47)
Independence of Mechanism (2:55)
Applied Causal Inference; Identification and Estimation of Causal Effects from Data
Motivating Causal Effect Inference (6:03)
Causal Effects and Common Cause (6:55)
Simpson's Paradox (5:29)
Defining Causal Inference with Interventions (6:44)
Recap on Causal Generative Reasoning Systems (4:08)
Identifiability for Causal Queries (2:29)
"Do"-Calculus: Identification without a Generative Model (2:07)
A Closer Look at the Do-Calculus
Valid Adjustment Sets and the Adjustment Formula (4:57)
Front-door Adjustment (1:50)
Quiz on Valid Adjustment and Causal Inference
Statistical Methods for Causal Inference
Instrumental Variables (10:47)
Causality and "The Bitter Lesson"
Propensity Scores and Matching (3:58)
Inverse Probability Reweighting Methods (4:17)
You can use Causal Generative Models for Causal Inference (10:16)
Potential Outcomes and their Contrasts to Causal Graphical Models
Potential Outcomes Framework and Assumptions (8:11)
SCMs vs PO; Heterogeneous Populations vs Individual Treatment Effects
"So then are SCM's better than PO?" and other FAQ
G-Methods, G-Estimation, and Time-varying Treatments
Algorithmic Structural Counterfactuals
Introduction to Counterfactual Reasoning (3:46)
The Twin-World Counterfactual Inference Algorithm (9:18)
Assessment - Counterfactuals
Mediation: An Algorithmic Bias Case Study
Assessment - Mediation
Single World Counterfactuals & Effect of Treatment on the Treated
Probabilities of Neccessity and Sufficiency
Picking an SCM
Assessment - Necessity and Sufficiency
Wrapping Up
You did it! Next steps.
Programming Causality
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