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Melting together prediction and inference

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, (cid:98) β . 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, (cid:98) Y . As DMC 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 inferen-tial 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, (cid:98) τ. 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.