统计建模：三种文化
Statistical modeling: the three cultures
摘要：
二十年前，Leo Breiman为统计建模确定了两种文化。数据建模文化（DMC）是指旨在对一个或多个感兴趣的量进行统计推断的实践。算法建模文化（AMC）是指定义机器学习（ML）过程的实践，该过程可以生成关于感兴趣事件的准确预测。Breiman认为，统计学家应该更多地关注AMC而不是DMC，因为ML在适应数据方面的优势。虽然20年后，由于数据科学革命，DMC在统计学中失去了一些主导作用，但我们观察到，这种文化仍然是自然科学和社会科学的主要实践。DMC是由于受既定科学方法的影响而形成的惯用方法，称为假设-演绎科学方法。尽管AMC与这种科学方法不相容，但在一些研究小组中，AMC和DMC文化强烈融合。我们认为，这种混合已经形成了一个肥沃的产卵池突变的文化，我们称之为混合建模文化（HMC）的预测和推理融合成新的程序，他们相互加强。本文确定了HMC的关键特征，从而促进了科学的努力，并推动了统计文化向更好的实践发展。所谓更好，我们指的是在分析因果关系时越来越可靠、有效和高效的统计实践。在推理和预测的结合中，HMC的结果是，预测和推理之间的区别在其极限下消失了。我们通过描述三种HMC实践来限定我们的融化论点，其中每种实践都捕获了科学周期的一个方面，即ML用于因果推断，ML用于数据采集，ML用于理论预测。
英文摘要：
Two decades ago, Leo Breiman identified two cultures for statistical modeling. The data modeling culture (DMC) refers to practices aiming to conduct statistical inference on one or several quantities of interest. The algorithmic modeling culture (AMC) refers to practices defining a machine-learning (ML) procedure that generates accurate predictions about an event of interest. Breiman argued that statisticians should give more attention to AMC than to DMC, because of the strengths of ML in adapting to data. While twenty years later, DMC has lost some of its dominant role in statistics because of the data-science revolution, we observe that this culture is still the leading practice in the natural and social sciences. DMC is the modus operandi because of the influence of the established scientific method, called the hypothetico-deductive scientific method. Despite the incompatibilities of AMC with this scientific method, among some research groups, AMC and DMC cultures mix intensely. We argue that this mixing has formed a fertile spawning pool for a mutated culture that we called the hybrid modeling culture (HMC) where prediction and inference have fused into new procedures where they reinforce one another. This article identifies key characteristics of HMC, thereby facilitating the scientific endeavor and fueling the evolution of statistical cultures towards better practices. By better, we mean increasingly reliable, valid, and efficient statistical practices in analyzing causal relationships. In combining inference and prediction, the result of HMC is that the distinction between prediction and inference, taken to its limit, melts away. We qualify our melting-away argument by describing three HMC practices, where each practice captures an aspect of the scientific cycle, namely, ML for causal inference, ML for data acquisition, and ML for theory prediction.
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