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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

Advanced Causal Inference in Machine Learning Systems

Prerequisites: ML, Causal Inference, Python

Level: Expert

What You'll Learn

Advanced Causal Discovery
Implement and evaluate state-of-the-art algorithms for discovering causal structures from observational and interventional data.
High-Dimensional Effect Estimation
Apply methods like Double Machine Learning and Causal Forests for estimating heterogeneous treatment effects in complex datasets.
Addressing Hidden Bias
Utilize techniques such as Instrumental Variables, RDD, DiD, and Proximal Inference to mitigate unobserved confounding and selection bias.
Causal Inference in Dynamic Settings
Analyze causal relationships in time-series data and dynamic treatment regimes.
System Integration
Integrate causal considerations and methods directly into machine learning development, evaluation, and monitoring pipelines.

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