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
Causal Forests for Heterogeneous Effects
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- Recursive partitioning for heterogeneous causal effects, Susan Athey, Guido W. Imbens, 2016 Proceedings of the National Academy of Sciences, Vol. 113 (National Academy of Sciences) DOI: 10.1073/pnas.1510489113 - This foundational paper introduces the Causal Forest algorithm, outlining the concepts of honest estimation and causal splitting criteria for estimating heterogeneous treatment effects.
- Estimation and Inference of Heterogeneous Treatment Effects using Causal Forests, Stefan Wager, Susan Athey, 2018 Journal of the American Statistical Association, Vol. 113 (Taylor & Francis) DOI: 10.1080/01621459.2017.1319835 - This paper provides a detailed statistical analysis and asymptotic theory for Causal Forests, including methods for constructing confidence intervals for CATE estimates.
- Generalized Random Forests, Susan Athey, Julie Tibshirani, Stefan Wager, 2019 The Annals of Statistics, Vol. 47 DOI: 10.1214/18-AOS1709 - This work presents the Generalized Random Forests (GRF) framework, extending Causal Forests to a wider class of local parameters and providing theoretical properties for estimation and inference.
- grf: Generalized Random Forests, Julie Tibshirani, Susan Athey, Erik Sverdrup, Stefan Wager, 2024 (CRAN) DOI: 10.32614/CRAN.package.grf - The official R package implementation of Generalized Random Forests, which includes Causal Forests. It provides practical tools for applying these methods and accessing their functionalities.