Melting together prediction and inference

Abstract:In Leo Breiman's influential article "Statistical modeling-the two cultures" he identified two cultures for statistical practices. The data modeling culture (DMC) denotes practices tailored for statistical inference targeting a quantity of interest, [inline-graphic 01]. The algorithmic modeling culture (AMC) refers to practices defining an algorithm, or a machine-learning (ML) procedure, that generates accurate predictions about an outcome of interest, [inline-graphic 02] was the dominant mode, Breiman argued that statisticians should give more attention to AMC. Twenty years later and energized by two revolutions—one in data-science and one in causal inference—a hybrid modeling culture (HMC) is rising. HMC fuses the inferential strength of DMC and the predictive power of AMC with the goal of analyzing cause and effect, and thus, HMC's quantity of interest is causal effect, [inline-graphic 03]. In combining inference and prediction, the result of HMC practices is that the distinction between prediction and inference, taken to its limit, melts away. While this hybrid culture does not occupy the default mode of scientific practices, we argue that it offers an intriguing novel path for applied sciences.