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
Evaluating ML Models Using Causal Concepts
Was this section helpful?
- Causality: Models, Reasoning, and Inference, Judea Pearl, 2009 (Cambridge University Press) DOI: 10.1017/CBO9780511803161 - A foundational text introducing structural causal models, do-calculus, and counterfactuals, essential for understanding the causal concepts used in model evaluation.
- Elements of Causal Inference: Foundations and Learning Algorithms, Jonas Peters, Dominik Janzing, Bernhard Schölkopf, 2017 (MIT Press) - A comprehensive book on the theoretical foundations of causal inference and its algorithms, bridging the gap between causality and machine learning.
- Causal Forests, Susan Athey, Stefan Wager, 2019 Observational Studies, Vol. 5 (University of Pennsylvania Press) DOI: 10.1353/obs.2019.0001 - Introduces Causal Forests, a method for estimating heterogeneous treatment effects, directly relevant to assessing predictive performance under interventions (CATE).
- Counterfactual Fairness, Matt J Kusner, Joshua Loftus, Chris Russell, Ricardo Silva, 2017 Advances in Neural Information Processing Systems, Vol. 30 (Curran Associates, Inc.) - Presents a formal definition of counterfactual fairness, which assesses if a model's prediction would change if a sensitive attribute were different while other factors remain constant.