Autoplay
Autocomplete
HTML5
Flash
Player
Speed
Previous Lecture
Complete and continue
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)
Reichenbach’s Common Cause Principle
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
Programming Causal Models
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)
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)
Generative Thinking vs. Population-based Thinking
SCMs Can Infer Individual Treatment Effects, PO Can't
"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)
Mediation: An Algorithmic Bias Case Study
Effect of Treatment on the Treated
Probabilities of Neccessity and Sufficiency
Causal Decision Theory
Intro to Decision Theory
Decision Theory is a Causal Problem
Decision Rules: Argmax, Minimax, Softmax
Statistical Hypothesis Testing, Bayes Rules, and Admissibility
Causality and Sequential Decision Processes
Reaction, Deliberation, Intention & Free Will
Sequential Decision Process as Causal DAGs
Intro to Markov Decision Processes
Markov Decision Processes as a Causal DAG
Policies and Interventions
The Bellman Equation in Causal Terms
Transition Functions as Structural Causal Models
Modeling Agents with Causal Probabilistic Programming
MDPs as Probabilistic Programs
Programming Policy as a Do-Operator
Context and the Do-Operator: Epidemic Example
The Do-Op for Introspecting Agents
Examples from Causal Game Theory
Planning as Inference: One-shot Policies
Planning as Inference: Programming MDPs
Causal Reinforcement Learning
Bandit Algorithms 101 (with Causal DAGs)
Bayesian Bandits and Bayesian Thompson Sampling
Causal Bandit Algorithms
Decision Theory is a Causal Problem
Lecture content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock