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
Score-based Discovery Algorithms: GES, LiNGAM
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- A polynomial-time algorithm for learning Bayesian networks with local structure, David M. Chickering, 2002 Machine Learning, Vol. 48 (Springer US) DOI: 10.1023/A:1015696117135 - Introduces the Greedy Equivalence Search (GES) algorithm, a method for causal discovery that searches over Markov equivalence classes.
- A Causal Discovery Algorithm for Linear Non-Gaussian Acyclic Models, Shohei Shimizu, Patrik O. Hoyer, Antti Hyvärinen, Aapo Tankasalo, 2006 Journal of Machine Learning Research, Vol. 7 - This paper presents the original LiNGAM framework, leveraging non-Gaussianity of error terms to identify full causal DAGs.
- Elements of Causal Inference: Foundations and Learning Algorithms, Jonas Peters, Dominik Janzing, Bernhard Schölkopf, 2017 (MIT Press) DOI: 10.7551/mitpress/9780262037380.001.0001 - A textbook covering foundational concepts and algorithms in causal inference, including discussions relevant to score-based methods and the identification principles used by LiNGAM.
- DirectLiNGAM: A direct method for learning a linear non-Gaussian acyclic model, Shohei Shimizu, Takuya Inazumi, Yasuhiro Sogawa, Akira Hyvarinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenji Bollen, 2011 Journal of Machine Learning Research, Vol. 12 (Journal of Machine Learning Research) DOI: 10.5555/2012000.2012061 - Describes DirectLiNGAM, an improvement to the original LiNGAM algorithm that directly estimates the causal order and coefficients, often used in practical applications.