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
Discovery Using Interventional Data
Was this section helpful?
- Causality: Models, Reasoning, and Inference, Judea Pearl, 2009 (Cambridge University Press) DOI: 10.1017/CBO9780511818311 - The foundational book introducing the do-calculus, structural causal models, and the theoretical role of interventions in causal discovery.
- Causation, Prediction, and Search, Peter Spirtes, Clark Glymour, Richard Scheines, 2000 (The MIT Press) - Introduces constraint-based causal discovery algorithms like PC and FCI, and establishes the concept of Markov Equivalence Classes, illustrating the need for interventional data.
- Elements of Causal Inference: Foundations and Learning Algorithms, Jonas Peters, Dominik Janzing, Bernhard Schölkopf, 2017 (The MIT Press) DOI: 10.7551/mitpress/9780262037310.001.0001 - A comprehensive textbook covering modern causal inference methods, including a section on causal discovery with interventional data, different intervention types, and Invariant Causal Prediction.
- Characterization and greedy search of Markov equivalence classes of interventional DAGs, Andreas Hauser, Peter Bühlmann, 2012 Journal of Machine Learning Research, Vol. 13 DOI: 10.5555/2361093.2361110 - Introduces the Greedy Interventional Equivalence Search (GIES) algorithm, a score-based method designed to learn causal graphs from a mix of observational and interventional data.
- Causal inference using invariant prediction: identification and confidence intervals, Jonas Peters, Peter Bühlmann, Nicolai Meinshausen, 2016 Journal of the Royal Statistical Society Series B: Statistical Methodology, Vol. 78 (Wiley) DOI: 10.1111/rssb.12167 - The original research paper presenting Invariant Causal Prediction (ICP), an approach that uses data from multiple environments to identify causal predictors.