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
Identification Beyond Standard Criteria
Was this section helpful?
- Causality: Models, Reasoning, and Inference, Judea Pearl, 2009 (Cambridge University Press) DOI: 10.1017/CBO9780511818817 - Definitive text on structural causal models, do-calculus rules, and identification theory, including the completeness of do-calculus and the ID algorithm.
- On the Identification of Causal Effects, Jin Tian, Judea Pearl, 2003 Journal of Machine Learning Research, Vol. 4 (Department of Computer Science, University of California, Los Angeles / UCLA Cognitive Systems Laboratory) - Introduces the complete ID algorithm for nonparametric identification of causal effects in arbitrary DAGs with hidden variables.
- Mostly Harmless Econometrics: An Empiricist's Companion, Joshua D. Angrist, Jörn-Steffen Pischke, 2009 (Princeton University Press) DOI: 10.1515/9781400829898 - Provides an in-depth treatment of instrumental variables, including assumptions, estimation, and practical considerations, from an econometric perspective.
- Identifying Causal Effects with Proxy Variables of an Unmeasured Confounder, Wang Miao, Zhi Geng, and Eric J. Tchetgen Tchetgen, 2018 Biometrika, Vol. 105 (Oxford University Press) DOI: 10.1093/biomet/asy038 - Introduces the framework for proximal causal inference, demonstrating how to identify causal effects using observed proxies for an unmeasured confounder.
- Causal Inference: What If, Miguel A. Hernán and James M. Robins, 2020 (CRC Press) DOI: 10.1201/9781351220491 - A widely referenced textbook covering various identification strategies, including those with unmeasured confounding, and discussing the assumptions underlying different methods.