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
Advanced Graphical Representations: DAGs
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- Causation, Prediction, and Search, Peter Spirtes, Clark Glymour, Richard Scheines, 2000 (MIT Press) DOI: 10.7551/mitpress/1754.001.0001 - A fundamental text in causal inference, detailing methods for causal discovery from observational data, including the use of Partial Ancestral Graphs (PAGs) to handle latent confounders.
- Causality: Models, Reasoning, and Inference, Judea Pearl, 2009 (Cambridge University Press) - The authoritative book on Structural Causal Models (SCMs) and graphical models for causal inference, providing comprehensive theory on identification, confounding, and bias, including discussions that necessitate advanced graphical representations.
- Ancestral graphs, Thomas S. Richardson, Peter Spirtes, 2002 The Annals of Statistics, Vol. 30 (Institute of Mathematical Statistics) DOI: 10.1214/aos/1031689015 - This paper formally introduces Maximal Ancestral Graphs (MAGs) and their properties, establishing m-separation as a criterion for conditional independence in the presence of latent variables.
- Elements of Causal Inference: Foundations and Learning Algorithms, Jonas Peters, Dominik Janzing, Bernhard Schölkopf, 2017 (MIT Press) - Provides a modern, machine learning-oriented perspective on causal inference, covering foundational concepts of SCMs and the challenges that arise in complex systems, leading to the need for advanced graphical models.