causal

Name: causal
Availability: InStock
Author: Semantica

Analyze causal chains and intervention impacts within the Semantica knowledge graph.

3.0k|350|Updated Jun 25, 2025

Search Tags:#audit #analyze #knowledge-graph #counterfactuals #interventions #causal #causal-chain

Ready to run

npx skills add https://github.com/semantica-agi/semantica --skill causal-semantica-agi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: causal
Source: https://github.com/semantica-agi/semantica/tree/main/plugins/skills/causal
Command: npx skills add https://github.com/semantica-agi/semantica --skill causal-semantica-agi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The skill analyzes causal relationships within the Semantica knowledge graph to uncover causal chains, test interventions, and compare counterfactual outcomes with measurable influence scores.

Core Features & Use Cases

Causal chain analysis for subjects or categories to reveal upstream and downstream drivers.
Intervention impact assessment and influence scoring to guide decision-making.
Counterfactual reasoning to explore alternate histories and potential outcomes.
Explainable provenance and traceability of causal inferences within the graph.

Quick Start

Analyze the causal chain for a subject by issuing the appropriate command, then review the results.

Frequently Asked Questions about causal

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze causal chains within a knowledge graph?▼

Causal chain analysis within a knowledge graph identifies upstream and downstream drivers for a given subject. It traces relationships to reveal how specific decisions influence related outcomes.

What is the best way to evaluate intervention impacts using graph data?▼

Evaluating intervention impacts involves applying influence scoring to graph data. This measures the effects of specific changes, helping guide decision-making by quantifying potential outcomes.

Can I generate counterfactual scenarios to compare alternate outcomes?▼

Yes, counterfactual reasoning explores alternate histories and potential outcomes. This allows you to compare counterfactual scenarios with measurable influence scores to understand different paths.

Does this causal analysis approach provide provenance and traceability?▼

Yes, causal analysis provides explainable provenance and traceability of causal inferences within the graph. This ensures all identified causal chains and scores can be audited.

What components do I need to return causal chains and influence scores?▼

To return causal chains and influence scores, the process leverages a CausalChainAnalyzer and related graph store components. These components map the operational API to extract decisions.