Enough?

We provide a critical response to Aronow et al. (2021) which argued that randomized controlled trials (RCTs) are "enough," while nonparametric identification in observational studies is not. We first investigate what is meant by "enough," arguing that this is a fundamentally a sociological claim about the relationship between statistical work and relevant institutional processes (here, academic peer review), rather than something that can be decided from within the logic of statistics. For a more complete conception of "enough," we outline all that would need to be known - not just knowledge of propensity scores, but knowledge of many other spatial and temporal characteristics of the social world. Even granting the logic of the critique in Aronow et al. (2021), its practical importance is a question of the contexts under study. We argue that we should not be satisfied by appeals to intuition or experience about the complexity of "naturally occurring" propensity score functions. Instead, we call for more empirical metascience to begin to characterize this complexity. We apply this logic to the case of recommender systems as a demonstration of the weakness of allowing statisticians' intuitions to serve in place of metascientific data. This may be, as Aronow et al. (2021) claim, one of the "few free lunches in statistics"-but like many of the free lunches consumed by statisticians, it is only available to those working at a handful of large tech firms. Rather than implicitly deciding what is "enough" based on statistical applications the social world has determined to be most profitable, we are argue that practicing statisticians should explicitly engage with questions like "for what?" and "for whom?" in order to adequately answer the question of "enough?"