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Causal Agents and Reinforcement Learning
Causal Decision Theory
Decision Theory Primer
Decision Theory as a Causal Problem
Decision Rules: Argmax, Minimax, and Softmax
Statistical Hypotheses, Bayes Rules, and Dominance
Causality and Sequential Decision Processes
Sequential Decision Process as Causal DAGs
Intro to Markov Decision Processes
Markov Decision Processes as a Causal DAG
Causal Reinforcement Learning
Reaction, Deliberation, Intention & Free Will
Policies and Interventions
The Bellman Equation in Causal Terms
Transition Functions as Structural Causal Models
Causal Reinforcement Learning
A case study in adversarial causal decision-making
Causal Bandits
Bandit Algorithms 101 (with Causal DAGs)
Bayesian Bandits and Bayesian Thompson Sampling
Causal Adversarial Bandit Algorithms
Modeling Agents with Causal Probabilistic Programming
MDPs as Probabilistic Programs
Programming Policy as a Do-Operator
Context and the Do-Operator: Epidemic Example
Modeling Agents with Causal Probabilistic Programming (Part 2)
The Do-Op for Introspecting Agents
Planning as Inference: One-shot Policies
Planning as Inference: Programming MDPs
Planning as Inference: One-shot Policies
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