# Liberty A Causal Framework For Benchmarking

> 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...

- Skill: `adu2021/liberty-a-causal-framework-for-benchmarking` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/liberty-a-causal-framework-for-benchmarking`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/liberty-a-causal-framework-for-benchmarking/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/liberty-a-causal-framework-for-benchmarking

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## 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.