Why use Causal Inference instead of standard ML?

Standard ML often fails under distribution shifts. Causal models are more robust because they capture the invariant mechanisms of the world, making them more reliable for out-of-distribution generalization.

What is System 2 deep learning in this context?

System 2 deep learning refers to the integration of slow, deliberate, and logical reasoning into neural networks, allowing them to process causal structures rather than just statistical correlations.

Does this skill support causal discovery?

Yes, it includes implementation patterns for the PC algorithm for constraint-based discovery and GFlowNets for sampling a distribution of likely causal graphs from data.

How does the skill handle counterfactual reasoning?

It provides a three-step framework: Abduction (inferring hidden variables), Action (applying a hypothetical intervention), and Prediction (computing the resulting outcome in a counterfactual world).

Causal Inference System 2

Name: Causal Inference System 2
Author: plurigrid

byplurigrid

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データサイエンスとML

Implements interventional and counterfactual reasoning patterns for deliberate System 2 deep learning and causal world modeling.

概要

The Causal Inference skill empowers Claude to move beyond correlational pattern matching (System 1) into the realm of deliberate reasoning (System 2). Based on the works of Judea Pearl and Yoshua Bengio, this skill provides structural causal models (SCM), interventional logic ('doing'), and counterfactual analysis ('what if'). It is designed for developers building robust AI systems that need to maintain performance across distribution shifts, understand causal structures through GFlowNets, and implement complex world models with identifiable mechanisms.

主な機能

Counterfactual analysis via abduction-action-prediction workflows
Structural Causal Model (SCM) implementation and manipulation
2 GitHub stars
Causal discovery patterns using PC algorithms and GFlowNets
Interventional reasoning using Pearl's Causal Hierarchy
System 2 architecture patterns for hybrid neural-causal networks

ユースケース

Improving model generalization and transfer learning across different domains
Analyzing historical datasets to predict outcomes of hypothetical interventions
Building robust world models for autonomous agents and decision-making systems

概要

主な機能

Counterfactual analysis via abduction-action-prediction workflows
Structural Causal Model (SCM) implementation and manipulation
2 GitHub stars
Causal discovery patterns using PC algorithms and GFlowNets
Interventional reasoning using Pearl's Causal Hierarchy
System 2 architecture patterns for hybrid neural-causal networks

ユースケース

Improving model generalization and transfer learning across different domains
Analyzing historical datasets to predict outcomes of hypothetical interventions
Building robust world models for autonomous agents and decision-making systems