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
Regression Discontinuity Designs (RDD)
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- Mostly Harmless Econometrics: An Empiricist's Companion, Joshua D. Angrist, Jörn-Steffen Pischke, 2008 (Princeton University Press) DOI: 10.1515/9781400829878 - Offers a broad yet rigorous introduction to RDD as a quasi-experimental method, covering its logic, assumptions, and practical implementation within a wider causal inference framework.
- Regression Discontinuity Designs: A Guide to Practice, Guido W. Imbens and Thomas Lemieux, 2008 Journal of Econometrics, Vol. 142 (Elsevier B.V.) DOI: 10.1016/j.jeconom.2007.05.004 - A highly cited paper providing a comprehensive overview of RDD, including sharp and fuzzy designs, identification assumptions, estimation strategies using local polynomial regressions, and practical considerations for applied work.
- Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test, Justin McCrary, 2008 Journal of Econometrics, Vol. 142 (Elsevier) DOI: 10.1016/j.jeconom.2007.05.005 - Introduces the widely used density test for detecting strategic manipulation of the running variable at the cutoff, a critical diagnostic for RDD validity.
- Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs, Sebastian Calonico, Matias D. Cattaneo, and Rocio Titiunik, 2014 Econometrica, Vol. 82 (Wiley-Blackwell) DOI: 10.3982/ECTA11757 - Presents methods for constructing robust bias-corrected confidence intervals for RDD estimators, addressing issues like bandwidth selection and kernel choice, which are standard in modern RDD practice.