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
Causal Principles in Feature Engineering and Selection
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- Causal Inference: What If, Miguel A. Hernán and James M. Robins, 2020 (Boca Raton: Chapman & Hall/CRC) - This comprehensive textbook provides a thorough introduction to causal inference, covering directed acyclic graphs (DAGs), identification of confounding, selection bias (including colliders), and mediation, which are fundamental for causal feature management.
- Elements of Causal Inference: Foundations and Learning Algorithms, Jonas Peters, Dominik Janzing, and Bernhard Schölkopf, 2017 (The MIT Press) DOI: 10.7551/mitpress/9780262037310.001.0001 - This book offers a theoretical background for causal inference, with an emphasis on its connection to machine learning. It details causal graphs, confounding, and principles of causal discovery, relevant for understanding data-generating processes for feature selection.
- Proximal Causal Learning: Principles and Design, Longqi Wang, Eric J. Tchetgen Tchetgen, Max G'Sell, Susan Athey, Stefan Wager, Zhiwei Qi, Zhenyu Zhang, Bo Zhang, and Xiang Zhou, 2023 Journal of Machine Learning Research, Vol. 24 DOI: 10.55982/jmlr.2023.22.0941 - This journal article provides a framework and review of Proximal Causal Inference, a method for estimating causal effects in the presence of unobserved confounders using proxy variables. This technique is specifically mentioned in the section.