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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Data Science & ML

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

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.

Key Features

01Counterfactual analysis via abduction-action-prediction workflows

02Structural Causal Model (SCM) implementation and manipulation

032 GitHub stars

04Causal discovery patterns using PC algorithms and GFlowNets

05Interventional reasoning using Pearl's Causal Hierarchy

06System 2 architecture patterns for hybrid neural-causal networks

Use Cases

01Improving model generalization and transfer learning across different domains

02Analyzing historical datasets to predict outcomes of hypothetical interventions

03Building robust world models for autonomous agents and decision-making systems

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi causal-inference

For use in Claude.ai and ChatGPT

Download Skill