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
Validation and Calibration of CATE Estimators
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- Estimation and inference for heterogeneous treatment effects using causal forests, Susan Athey and Stefan Wager, 2019 Journal of the American Statistical Association, Vol. 114 (Taylor & Francis) DOI: 10.1080/01621459.2017.1411512 - Introduces Causal Forests, a prominent CATE estimator, and discusses methods for evaluating heterogeneous treatment effects.
- Double/debiased machine learning for treatment and causal parameters, Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins, 2018 The Econometrics Journal, Vol. 21 (Wiley) DOI: 10.1111/ectj.12097 - Provides the theoretical foundation for Double Machine Learning, relevant for constructing pseudo-outcomes and robust ATE estimation in validation.
- Identifying and Validating Optimal Treatment Regimes Using Uplift Modeling, Frederik Devriendt, Bjoern Berrevoets, and Wouter Verbeke, 2018 Journal of Marketing Analytics, Vol. 6 (Springer Nature) DOI: 10.1057/s41270-017-0030-x - Focuses on uplift modeling, providing insights into gain curves (AUUC) and their use in validating heterogeneous treatment effect models.