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
Techniques for High-Dimensional Confounders
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- Double/debiased machine learning for treatment effects, Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins, 2018 American Economic Review, Vol. 108 DOI: 10.1257/aer.20170829 - Introduces Double Machine Learning (DML), a method for estimating causal effects that combines machine learning with Neyman orthogonality to achieve robust, asymptotically normal inference in high-dimensional settings.
- Estimation and inference of heterogeneous treatment effects using random forests, Stefan Wager, Susan Athey, 2018 Journal of the American Statistical Association, Vol. 113 (Taylor & Francis) DOI: 10.1080/01621459.2017.1319839 - Presents Causal Forests, an adaptation of random forests for estimating heterogeneous treatment effects and constructing valid confidence intervals.
- High-dimensional penalized least squares in economics, Alexandre Belloni, Victor Chernozhukov, Christian Hansen, 2014 Econometrica, Vol. 82 (Wiley-Blackwell) DOI: 10.3982/ECTA10729 - Discusses the use of Lasso and other penalized regression methods for estimating high-dimensional nuisance functions in econometric models, relevant for robust causal inference.
- Causality, Judea Pearl, 2009 (Cambridge University Press) DOI: 10.1017/CBO9780511803161 - A foundational text on causal inference, introducing graphical models, the do-calculus, and criteria for identifying causal effects, including the backdoor criterion for adjustment sets. (2nd edition)
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