Causal Machine Learning & Counterfactual Inference

Explore causal machine learning, treatment effect estimation, and counterfactual reasoning for reliable AI decision systems.


Deep dives into causal inference, treatment effect estimation, and counterfactual reasoning for robust decision-making.




Causal Machine Learning

Causal Inference in ML Pipelines: Beyond Feature Flattening

[Series] Why Machine Learning Fails in Production and How to Fix It with Causal Inference

Machine LearningDeep LearningData SciencePython

Predictive machine learning architectures optimize for empirical risk under static environments, making them highly dangerous when deployed as policy engines.

When a predictive model flattens chronologically ordered data, it inevitably treats downstream symptoms as actionable policy levers—a liability known as the feature flattening trap.

This architectural blueprint deep-dives into measure-theoretic causal structural models, graph surgery via Judea Pearl’s $do$-operator, and structural backdoor adjustments to unlock true off-policy evaluation directly from biased historical logs.

Causal Inference in ML Pipelines: Beyond Feature Flattening

Kernel Labs | Kuriko IWAI | kuriko-iwai.com

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