Implement sophisticated causal inference techniques within machine learning systems. This course covers advanced methods for causal discovery, effect estimation with high-dimensional data, handling unobserved confounding, and integration into ML pipelines for expert practitioners seeking to build more reliable and interpretable AI systems.
Prerequisites ML, Causal Inference, Python
Level:
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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