liberty-a-causal-framework-for-benchmarking

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.