Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data is available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), based on a general mutual information supremum principle, which greatly improves the precision of inferred causal relations while distinguishing genuine causes from putative and latent causal effects. We showcase iMIIC on synthetic and real-life healthcare data from 396,179 breast cancer patients from the US Surveillance, Epidemiology, and End Results program. More than 90\% of predicted causal effects appear correct, while the remaining unexpected direct and indirect causal effects can be interpreted in terms of diagnostic procedures, therapeutic timing, patient preference or socio-economic disparity. iMIIC's unique capabilities open up new avenues to discover reliable and interpretable causal networks across a range of research fields.

翻译：揭示因果效应是科学研究的核心，但在仅有观测数据可用时仍具挑战性。实践中，因果网络难以学习且不易解释，且仅适用于相对较小的数据集。我们提出了一种更可靠且可扩展的因果发现方法（iMIIC），该方法基于通用互信息上确界原理，显著提高了推断因果关系的精确性，同时区分了真实因果效应与假定及潜在因果效应。我们在来自美国监测、流行病学及最终结果计划的396,179名乳腺癌患者的合成与真实医疗数据上展示了iMIIC的性能。超过90%的预测因果效应正确无误，而其余意料之外的直接与间接因果效应可从诊断流程、治疗时机、患者偏好或社会经济差异等角度进行解释。iMIIC的独特能力为在多个研究领域发现可靠且可解释的因果网络开辟了新途径。