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Advanced Causal Inference in Machine Learning Systems

Chapter 1: Structural Causal Models and Identification Strategies

Structural Causal Models Revisited

Advanced Graphical Representations: DAGs and Beyond

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

Causal Inference for Temporal Data and Dynamic Systems

Sections

5.1 Challenges in Causal Inference with Time Series
5.2 Granger Causality: Formulation and Shortcomings
5.3 Structural Vector Autoregression (SVAR)
5.4 Dynamic Treatment Regimes and Estimation
5.5 Time Series Causal Discovery Methods
5.6 Causal Reinforcement Learning and Off-Policy Evaluation
5.7 Practice: Temporal Causal Analysis

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