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

Fixing Selection Bias in Enterprise Data with Inverse Propensity Scoring (IPS)

[Series] Causal Machine Learning - 2. De-Biasing Historical Data Logs (without Expensive A/B Tests)

Machine LearningDeep LearningData SciencePython

Standard machine learning models fail when observational data is poisoned by selection bias—mistaking baseline confounding for true treatment impact.

Inverse Propensity Scoring (IPS) provides an offline mathematical simulator that transforms biased operational logs into synthetic Randomized Controlled Trials (RCTs).

This guide breaks down the underlying propensity mathematics, positivity constraints, and production Python code to calculate unconfounded Average Treatment Effects (ATE).

Fixing Selection Bias in Enterprise Data with Inverse Propensity Scoring (IPS)

Kernel Labs | Kuriko IWAI | kuriko-iwai.com

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Why Machine Learning Demands Causal Inference

[Series] Causal Machine Learning - 1. Rethinking the Predictive Paradigm

Machine LearningDeep LearningData SciencePython

Standard machine learning models excel at empirical risk minimization on static observational data, but fail catastrophically when an operational policy actively alters the data-generating distribution.

This advanced guide exposes the feature flattening trap—where models mistake downstream symptoms for upstream causes—and details how to deploy structurally invariant causal models using Judea Pearls Backdoor Criterion and Off-Policy Evaluation (OPE).

Why Machine Learning Demands Causal Inference

Kernel Labs | Kuriko IWAI | kuriko-iwai.com

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