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
Difference-in-Differences (DiD) with Panel Data
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- Mostly Harmless Econometrics: An Empiricist's Companion, Joshua D. Angrist and Jörn-Steffen Pischke, 2009 (Princeton University Press) - A classic and accessible textbook providing an intuitive introduction to causal inference methods, including a detailed treatment of Difference-in-Differences.
- Difference-in-Differences with Multiple Time Periods and Heterogeneous Treatment Effects, Brantly Callaway, Pedro H. C. Sant'Anna, 2021 Journal of Econometrics, Vol. 225 (Elsevier) DOI: 10.1016/j.jeconom.2020.12.001 - Introduces a robust Difference-in-Differences estimator for settings with multiple time periods and staggered treatment adoption, addressing bias concerns of traditional fixed effects models.
- Difference-in-Differences with Variation in Treatment Timing, Andrew Goodman-Bacon, 2021 Journal of Econometrics, Vol. 225 (Elsevier) DOI: 10.1016/j.jeconom.2021.03.001 - Analyzes the bias in the two-way fixed effects Difference-in-Differences estimator when treatment timing varies, providing a decomposition to understand the sources of bias.
- Econometric Analysis of Cross Section and Panel Data, Jeffrey M. Wooldridge, 2010 (MIT Press) - A comprehensive graduate-level econometrics textbook that provides a rigorous treatment of panel data methods, including Difference-in-Differences and fixed effects models.