Concept-based explanations quantify how high-level concepts (e.g., gender or experience) influence model behavior, which is crucial for decision-makers in high-stakes domains. Recent work evaluates the faithfulness of such explanations by comparing them to reference causal effects estimated from counterfactuals. In practice, existing benchmarks rely on costly human-written counterfactuals that serve as an imperfect proxy. To address this, we introduce a framework for constructing datasets con...
Scanned 9/9/2026
Install to Claude Code
npx -y skills add ADu2021/skillXiv --skill liberty-a-causal-framework-for-benchmarking --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Liberty A Causal Framework For Benchmarking?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/adu2021-liberty-a-causal-framework-for-benchmarking)More formats (shields.io, HTML) on the badges page.
---
name: liberty-a-causal-framework-for-benchmarking
title: "LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanation"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: "https://arxiv.org/abs/2601.10700"
keywords: [Benchmark]
description: "Concept-based explanations quantify how high-level concepts (e.g., gender or experience) influence model behavior, which is crucial for decision-makers in high-stakes domains. Recent work evaluates the faithfulness of such explanations by comparing them to reference causal effects estimated from counterfactuals. In practice, existing benchmarks rely on costly human-written counterfactuals that serve as an imperfect proxy. To address this, we introduce a framework for constructing datasets contai..."
---
## Overview
This skill covers research on liberty: a causal framework for benchmarking concept-based explanation. It addresses important challenges in agent development and evaluation.
## Key Insights
The paper provides:
- Novel approaches or frameworks for agent systems
- Empirical evaluation results and benchmarks
- Generalizable principles for practitioners
## When to Use
Use this skill when working on:
- Agent-based systems and applications
- Autonomous reasoning and planning
- Agent performance evaluation and improvement
## When NOT to Use
- For non-agent-related tasks
- When seeking implementation code (consult the paper)
## Resources
- ArXiv Abstract: https://arxiv.org/abs/2601.10700
- Full PDF: https://arxiv.org/pdf/2601.10700
- HTML: https://arxiv.org/html/2601.10700
Refer to the original paper for complete technical details, methodology, and experimental protocols.
Is this your skill, or is something wrong with this listing? . Author removals are honored within 72 hours.
No comments yet. Be the first to comment!