We draw a distinction between the traditional reference class problem, which describes an obstruction to estimating a single individual probability—which we rename the individual reference class problem—and what we call the reference class problem at scale, which can result when using tools from statistics and machine learning to systematically make predictions about many individual probabilities simultaneously. We argue that scale actually helps to mitigate the reference class problem, and purely statistical tools can be used to efficiently minimize the reference class problem at scale, even though they cannot be used to solve the individual reference class problem.

This paper proposes a novel view in the the philosophy of race & causation literature known as “causal agnosticism” about race. Causal agnosticism about race implies that it is reasonable to refrain from making judgments about whether race is a cause. The paper’s thesis asserts that certain conditions must be met to infer that something is a cause, according to the fundamental assumptions of causal inference. However, in the case of race, these conditions are often violated. By advocating for causal agnosticism, the paper suggests a more modest approach to understanding the role of race in causal relationships.