While witnessing the exceptional success of machine learning (ML) technologies in many applications, users are starting to notice a critical shortcoming of ML: correlation is a poor substitute for causation. The conventional way to discover causal relationships is to use randomized controlled experiments (RCT); in many situations, however, these are impractical or sometimes unethical. Causal learning from observational data offers a promising alternative. While being relatively recent, causal learning aims to go far beyond conventional machine learning, yet several major challenges remain. Unfortunately, advances are hampered due to the lack of unified benchmark datasets, algorithms, metrics, and evaluation service interfaces for causal learning. In this paper, we introduce {\em CausalBench}, a transparent, fair, and easy-to-use evaluation platform, aiming to (a) enable the advancement of research in causal learning by facilitating scientific collaboration in novel algorithms, datasets, and metrics and (b) promote scientific objectivity, reproducibility, fairness, and awareness of bias in causal learning research. CausalBench provides services for benchmarking data, algorithms, models, and metrics, impacting the needs of a broad of scientific and engineering disciplines.

翻译：尽管见证了机器学习（ML）技术在众多应用中的非凡成功，用户开始注意到ML的一个关键缺陷：相关性是因果关系的拙劣替代品。发现因果关系的传统方法是使用随机对照实验（RCT）；然而，在许多情况下，这些实验不切实际，有时甚至不道德。从观测数据中进行因果学习提供了一个有前景的替代方案。因果学习虽然相对较新，但其目标远超传统机器学习，然而仍存在若干重大挑战。遗憾的是，由于缺乏统一的基准数据集、算法、指标以及因果学习的评估服务接口，相关进展受到阻碍。本文中，我们介绍{\em CausalBench}，一个透明、公平且易于使用的评估平台，旨在（a）通过促进新算法、数据集和指标方面的科学合作，推动因果学习研究的进展，以及（b）在因果学习研究中促进科学客观性、可重复性、公平性以及对偏见的认识。CausalBench为基准测试数据、算法、模型和指标提供服务，满足广泛的科学与工程学科的需求。