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
Time Series Causal Discovery Methods
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- Causal network reconstruction from time series: From statistics to applications, Jakob Runge, Lydia Nowack, Marlene Kretschmer, Veronika Eyring, Manfred Milinski, 2018 Proceedings of the National Academy of Sciences, Vol. 115 (National Academy of Sciences) DOI: 10.1073/pnas.1714782115 - Presents the foundational PCMCI algorithm for causal discovery in time series, detailing its two-stage approach to handle auto-dependencies and high dimensionality.
- Discovering causality in complex systems with tigramite, Jakob Runge, 2020 Journal of Open Source Software, Vol. 5 (Open Journals) DOI: 10.21105/joss.02470 - Describes the
tigramitePython library, a comprehensive framework for applying PCMCI and other time series causal discovery methods, offering various conditional independence tests. - A review of causal discovery algorithms for time series data, Muhammad Malik, Shahbaz Saquib, Imtiaz Shafi, 2022 Artificial Intelligence Review, Vol. 55 (Springer Netherlands) DOI: 10.1007/s10462-021-10041-3 - A recent survey paper that provides an overview of various causal discovery algorithms adapted for time series data, including constraint-based and score-based methods mentioned in the section.
- Elements of Causal Inference: Foundations and Learning Algorithms, Jonas Peters, Dominik Janzing, Bernhard Schölkopf, 2017 (MIT Press) - A foundational textbook on causal inference that covers structural causal models, conditional independence testing, and the PC algorithm, which are fundamental to understanding constraint-based methods like PCMCI.