Scholarlink

We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods. AutoCD’s goal is to deliver all causal information that an expert human analyst would provide and answer user’s causal queries. To this goal, we introduce ETIA, a system that performs dimensionality reduction, causal structure learning, and causal reasoning. We present the architecture of ETIA, benchmark its performance on synthetic data sets, and present a use case example. The system is general and can be applied to a plethora of causal discovery problems.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

ETIA: Towards an Automated Causal Discovery Pipeline

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

查看全文
  • Konstantina Biza,
  • Antonios Ntroumpogiannis,
  • Sofia Triantafillou,
  • Ioannis Tsamardinos

摘要

We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods. AutoCD’s goal is to deliver all causal information that an expert human analyst would provide and answer user’s causal queries. To this goal, we introduce ETIA, a system that performs dimensionality reduction, causal structure learning, and causal reasoning. We present the architecture of ETIA, benchmark its performance on synthetic data sets, and present a use case example. The system is general and can be applied to a plethora of causal discovery problems.