
TL;DR
This paper critiques the notion of statistical sufficiency in observational studies, emphasizing the sociological aspects of what constitutes 'enough' knowledge and advocating for empirical metascience to better understand complexity.
Contribution
It challenges the idea of a universal 'enough' in statistical research, highlighting the importance of social context and proposing empirical metascience to assess complexity.
Findings
Statistical sufficiency is a sociological rather than purely technical concept.
Empirical data can better inform what constitutes 'enough' knowledge.
Practitioners should explicitly consider social context in statistical decision-making.
Abstract
We respond to Aronow et al. (2025)'s paper arguing that randomized controlled trials (RCTs) are "enough," while nonparametric identification in observational studies is not. We agree with their position with respect to experimental versus observational research, but question what it would mean to extend this logic to the scientific enterprise more broadly. We first investigate what is meant by "enough," arguing that this is fundamentally a sociological claim about the relationship between statistical work and larger social and institutional processes, 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…
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Taxonomy
TopicsComputational and Text Analysis Methods · Advanced Causal Inference Techniques · Data Analysis with R
