Advanced Causal Inference in Machine Learning Systems
Chapter 1: Structural Causal Models and Identification Strategies
Structural Causal Models Revisited
Advanced Graphical Representations: DAGs
Do-calculus Rules and Applications
Identification Beyond Standard Criteria
Addressing Cycles and Feedback in Causal Graphs
Sensitivity Analysis for Identification Assumptions
Practice: Applying Identification Logic
Chapter 2: Advanced Causal Discovery Techniques
Constraint-based Discovery Algorithms: PC, FCI Extensions
Score-based Discovery Algorithms: GES, LiNGAM
Discovery Using Interventional Data
Causal Discovery from Heterogeneous Data
Assessing Causal Discovery Algorithm Performance
Managing Latent Variables in Discovery
Hands-on Practical: Implementing Discovery Algorithms
Chapter 3: Estimating Causal Effects in High Dimensions
Double Machine Learning for Average Treatment Effects
Causal Forests for Heterogeneous Effects
Meta-Learners for CATE Estimation
Deep Learning Approaches for Effect Estimation
Techniques for High-Dimensional Confounders
Validation and Calibration of CATE Estimators
Practice: DML and Causal Forest Implementation
Chapter 4: Confronting Unobserved Confounding and Selection Bias
Advanced Instrumental Variables (IV) Methods
Deep Learning and Kernel Methods for IV
Regression Discontinuity Designs (RDD)
Difference-in-Differences (DiD) with Panel Data
Adapting Selection Bias Correction Methods
Proximal Causal Inference Principles
Hands-on Practical: IV and RDD Analysis
Chapter 5: Causal Inference for Temporal Data and Dynamic Systems
Challenges in Causal Inference with Time Series
Granger Causality: Formulation and Shortcomings
Structural Vector Autoregression (SVAR)
Dynamic Treatment Regimes and Estimation
Time Series Causal Discovery Methods
Causal Reinforcement Learning and Off-Policy Evaluation
Practice: Temporal Causal Analysis
Chapter 6: Operationalizing Causal Inference in Machine Learning Pipelines
Causal Principles in Feature Engineering and Selection
Causality-Aware Model Development
Evaluating ML Models Using Causal Concepts
Enhancing A/B Testing with Causal Inference
Monitoring ML Systems for Causal Stability
Designing Causal Inference Components for MLOps
Hands-on Practical: Building a Causally-Informed Pipeline
Do-calculus Rules and Applications
Was this section helpful?
- Causality: Models, Reasoning and Inference, Judea Pearl, 2009 (Cambridge University Press) DOI: 10.1017/CBO9780511803161 - This foundational book introduces Structural Causal Models and the do-calculus rules, providing a comprehensive treatment of causal inference.
- Causal Inference in Statistics: A Primer, Judea Pearl, Madelyn Glymour, Nicholas P. Jewell, 2016 (Wiley) - An accessible introduction to causal inference, covering do-calculus, identification strategies, and graphical models from a statistical perspective.
- A general identification condition for causal effects, Jin Tian, Judea Pearl, 2002 Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02) (AAAI Press) DOI: 10.1609/aaai.v18i1.18956 - This paper presents the completeness proof for the do-calculus, demonstrating that if a causal effect is identifiable, do-calculus can derive its formula.