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
Adapting Selection Bias Correction Methods
Was this section helpful?
- Sample Selection Bias as a Specification Error, James J. Heckman, 1979 Econometrica, Vol. 47 (Wiley-Blackwell) DOI: 10.2307/1912352 - The seminal paper introducing the Heckman two-step correction procedure for addressing sample selection bias in econometric models.
- Mostly Harmless Econometrics: An Empiricist's Companion, Joshua D. Angrist, Jörn-Steffen Pischke, 2009 (Princeton University Press) - A widely-used textbook that provides an accessible introduction to causal inference methods in econometrics, including discussions on selection models and their identification challenges.
- The Heckman Correction for Sample Selection and its Critique, Patrick A. Puhani, 2000 Journal of Economic Surveys, Vol. 14 DOI: 10.1111/1467-6419.00104 - A comprehensive review of the Heckman correction, detailing its assumptions, identification issues (including the exclusion restriction), and various critiques.
- Marginal Structural Models and Causal Inference in Epidemiology, Miguel Ángel Hernán, Babette Brumback, James M. Robins, 2000 Epidemiology, Vol. 11 DOI: 10.1097/00001648-200009000-00008 - Introduces marginal structural models and inverse probability weighting for causal inference, providing context for weighting methods often adapted for selection bias correction.
- Generalized Control Functions with Machine Learning for Causal Inference, Xinyi Nie, Michael I. Jordan, and Stefan Wager, 2020 Advances in Neural Information Processing Systems, Vol. 33 (NeurIPS) - Explores how to extend and apply control functions, including those for selection bias, using flexible machine learning methods to improve causal effect estimation.