While the field of nanocatalysis has benefited from the application of conventional machine learning methods by leveraging the correlations between processing/structure/property variables, the outcomes from purely correlational studies lack actionability due to missing mechanistic insights. Statistical learning, particularly causal inference, can potentially provide access to more actionable insights by allowing the discovery and verification of deeply obscured causal relationships between variables, using strong correlations identified from interpretable machine learning models as starting points. Recent studies that exemplify the collaborative usage of correlational and causal analysis in catalysis are discussed, including studies potentially benefiting from this approach. Some challenges remaining in the application of inference techniques to the field are identified and suggestions of future directions are provided.

虽然纳米催化领域通过利用加工/结构/性质变量之间的相关性而受益于传统机器学习方法的应用，但由于缺少机械洞察力，纯相关性研究的结果缺乏可操作性。统计学习，尤其是因果推理，可以通过使用从可解释的机器学习模型中识别出的强相关性作为起点，发现和验证变量之间非常模糊的因果关系，从而潜在地提供更多可操作的见解。讨论了最近的研究，这些研究举例说明了催化中相关分析和因果分析的协同使用，包括可能从这种方法中受益的研究。