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
Deep Learning and Kernel Methods for IV
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- Deep IV: A Flexible Approach to Instrumental Variable Estimation, Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy, 2017 Proceedings of the 34th International Conference on Machine Learning, Vol. 70 (PMLR) - Introduces the Deep IV framework, replacing linear models in 2SLS with neural networks to handle non-linear relationships and high-dimensional data in IV estimation.
- Nonparametric Instrumental Variable Estimation using Kernel Methods, Sameer Singh, K. P. Chaudhuri, Shujian Ma, 2019 Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Vol. 33 (AAAI Press) DOI: 10.1609/aaai.v33i01.33011045 - Presents a method for nonparametric IV estimation using kernel functions to model complex relationships and satisfy moment conditions.
- Causal Inference with Machine Learning: A Survey, Marzyeh Ghassemi, Tristan Naumann, Joshua V. Smith, Andrew C. Miller, 2021 Foundations and Trends® in Machine Learning, Vol. 14 (Now Publishers) DOI: 10.1561/2200000094 - Provides an overview of how machine learning techniques are applied to causal inference, including discussions on IV methods.
- Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Bernhard Schölkopf, Alexander J. Smola, 2001 (The MIT Press) - A foundational book introducing kernel methods, including Reproducing Kernel Hilbert Spaces (RKHS), which underpin Kernel IV techniques.
- EconML: A Python Package for Causal Inference in Machine Learning, Victor Chernozhukov, Susan Athey, Robert Schapire, Greg Lewis, Jason Hartford, David S. Matteson, Kevin Leyton-Brown, 2019 Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) (ACM) DOI: 10.1145/3292500.3330752 - Introduces the EconML library, which provides robust implementations of various causal inference methods, including advanced IV techniques like Deep IV.