Causal AI book cover

Causal Artificial Intelligence

  A Roadmap for Building Causally Intelligent Systems

  Elias Bareinboim


Draft version (Apr/19): link


Teaching & Slides (coming soon)

Lecture 1   Introduction
Ch. 1
  • Logistics; Motivation; Machine Learning.
    Pearl's Causal Hierarchy.
Lecture 2   Structural Causal Models
Ch. 2
  • Structural Causal Models; Causal Diagrams.
    Extra: CHT notes

Lecture 3   Identification of Causal Effects - Basics
Ch. 4, Sec. 4.1, 4.2 (< 4.2.1)
  • Intuition and Definition of Causal Effects.
    The Truncated Factorization Product.
    The Identification Problem
Lecture 4   The Problem of Confounding and the Back-door Criterion
Sec. 4.2.1
  • Identifiable and non-identifiable effects. Confounding Bias.
    The Backdoor Criterion. Inverse probability Weighting/Propensity Score.
Lecture 5   The Algorithmic Back-door Criterion
Sec. 4.2.2-4.2.3
  • The Conditional Backdoor Criterion.
    Adjustment-Backdoor Criterion. Poly-time delay Backdoor.
Lecture 6   The Interventional Calculus
Sec. 4.2.5, 4.2.6, 4.3
  • The Generalized Truncated Product.
    The Front-door Case.
    The interventional calculus (do-calculus).
Lecture 7   Causal Operators (L2) and Algorithmic Identification
Sec. 4.4
  • C-factors. C-Operators and Data Structure.
    Systematic ID.

Lecture 8   Counterfactuals Foundations
Ch. 5, Sec. 5.1, 5.2
  • Counterfactual's definition. Motivation. Hinton's paradox.
    Counterfactual Quantities & the Structural Basis Theorem
Lecture 9   The Counterfactual Calculus
Sec. 5.3
  • Counterfactual Contraints.
    Counterfactual Calculus.
Lecture 10   Causal Operators (L3) and Algorithmic Identification
Sec. 5.4
  • Ctf-factors and operators. Consistent ctf-factors. Systematic ID.
Lecture 11   Partial Identification
Ch. 5, Sec. 5.5
  • Bounding interventional distributions (bow graph and IV model).
    Causal offline-to-online Learning (COOL).
Lecture 12   The Sigma Calculus
Ch. 4, Sec. 4.6
  • Soft interventions. Sigma-Calculus.

Lecture 13   Fairness I
Ch. 6, Sec. 6.1-6.3
  • Theory of Decomposing Variations; Fundamental Problem of Causal Fairness; Explainability Plane.
    TV-family; Using contrastive measures in practice; Structure of the TV-family; Towards the Fairness Map.
Lecture 14   Fairness II
Sec. 6.4.1
  • Causal Interactions. Bias quantification (Task 1).
Lecture 15   Fairness III
Sec. 6.4.1-6.4.3
  • Fair Predictions (Task 2). Fair Decision-Making (Task 3).

Lecture 16   Decision-Making I
Ch. 7-8
  • Causal Decision Model. Comparison w/ MDPs. Causal RL Tasks.
    Off-police Learning. Online Learning. Causal identification.
Lecture 17   Decision-Making II
Sec. 9.1, 9.2, 9.5
  • Causal-offline-to-online Learning (COOL).
    Causal Imitation Learning.
    Causally-aligned Curriculum Learning.
Lecture 18   Decision-Making III
Sec. 9.3, 9.4, 9.6
  • Where to intervene.
    Counterfactual Decision-Making.
    Causal Game Theory.

Lecture 19   Generalizability I
Ch. 10, Sec. 11.1-11.2
  • Transportability Foundations; Direct Transportability; the Score TR algorithm.
    Transportable Representations; Causal Mechanistic Stability & invariance learning; Overview of the DG literature.
Lecture 20   Generalizability II
Ch. 11.3, 11.4.
  • Partial Transportability & Adaptation.
Lecture 21   Generalizability III
Ch. 12
  • Interventional & Counterfactual Transportability.

Lecture 22   Generative I
Ch. 13
  • Neural Causal Models. Structural Constraints.
    Causal Generative Modeling. Practical Implementation.
Lecture 23   Generative II
Ch. 14
  • Modeling and ACMs. Impossibility results.
    Ctf-consistent estimators. Experimental results.
Lecture 24   Generative III
Ch. 15
  • PCH's Abstraction. Inferences across Abstractions. Representation Learning.

Lecture 25   Learning I
Ch. 16
  • Observational Equivalence Class.
    Interventional Equivalence Class.
Lecture 26   Learning II
Ch. 17
  • Multi-domain Structural Learning.
Lecture 27   Learning III
Ch. 18
  • Causal Representation Learning.

Lecture 28   Parametric Identification
Ch. 19
  • Foundations of Linear SCMs. Causal Regression.
    Instrumental Variables. Instrumental Sets. Decompositions.
    Monotonic Identification. LATE.
Lecture 29   Causal Estimation
Ch. 20
  • Double Robutness.
Lecture 30   A Hierarchy of Graphical Models
Ch. 21
  • Other Inferential Systems.

Summary & Structure


Forthcoming.

Errata


Forthcoming.

About the Author


Forthcoming.

Contact & FAQ


Forthcoming.