Contents
1 Introduction
2 Causality: A Minimal Introduction
2.1 Bayesian Networks
2.2 Causal Bayesian Networks
2.3 Structural Causal Models
2.4 Causal Representation Learning
2.5 Spurious Relationships due to Confounding
2.6 Causal Estimand Identification
2.7 Causal Influence
3 Causal Supervised Learning
3.1 Invariant Feature Learning
3.2 Invariant Mechanism Learning
3.3 Open Problems
4 Causal Generative Modeling
4.1 Structural Assignment Learning
4.2 Causal Disentanglement
4.3 Open Problems
5 Causal Explanations
5.1 Feature Attribution Explanations
5.2 Contrastive Explanations
5.3 Open Problems
6 Causal Fairness
6.1 Two more detailed examples
6.2 Counterfactual Fairness Criteria
6.3 Interventional Fairness
6.4 Fairness under Distribution Shifts
6.5 Open Problems
7 Causal Reinforcement Learning
7.1 Isn't RL already "Causal"?
7.2 Causal Bandits
7.3 Model-Based RL
7.4 Multi-Task RL
7.5 Off-Policy Policy Evaluation
7.6 Imitation Learning
7.7 Credit Assignment
7.8 Counterfactual Data Augmentation
7.9 Agent Incentives
7.10 Open Problems
8 Modality-specific Applications
8.1 Causal Computer Vision
8.2 Causal Natural Language Processing
8.3 Causal Graph Representation Learning
9 Causal Benchmarks
9.1 Reinforcement Learning
9.2 Computer Vision
9.3 Natural Language Processing
10 The Good, the Bad and the Ugly
10.1 The Good
10.2 The Bad
10.3 The Ugly
11 Related Work
11.1 Other Surveys
11.2 Machine Learning for Causal Inference
12 Conclusion