Causal models and methods have great promise, but their progress has been stalled. Proposals using causality get squeezed between two opposing worldviews. Scientific perfectionism--an insistence on only using "correct" models--slows the adoption of causal methods in knowledge generating applications. Pushing in the opposite direction, the academic discipline of computer science prefers algorithms with no or few assumptions, and technologies based on automation and scalability are often selected for economic and business applications. We argue that these system-centric inductive biases should be replaced with a human-centric philosophy we refer to as scientific pragmatism. The machine learning community must strike the right balance to make space for the causal revolution to prosper.

翻译：因果模型与方法前景广阔，但其发展进程已陷入停滞。基于因果性的研究提案往往受到两种对立世界观的双重挤压。科学完美主义——即坚持仅使用"正确"模型——阻碍了因果方法在知识生成应用中的推广。另一方面，计算机科学学科更倾向于采用假设极少甚至无需假设的算法，而基于自动化和可扩展性的技术常因经济与商业考量被优先选用。我们认为，应当以我们称之为科学实用主义的人本哲学取代这些以系统为中心的归纳偏好。机器学习界必须找到恰当的平衡点，为因果革命的蓬勃发展创造空间。