🤖 AI Summary
Social science research urgently requires interpretable and reproducible exploratory discovery from unstructured text—without presupposing measurement constructs. This paper proposes an end-to-end framework addressing this challenge. First, it constructs a high-dimensional, semantically transparent, and interpretable concept dictionary via sparse coding and semantic modeling. Second, it introduces a novel high-dimensional multiple testing procedure that rigorously controls the k-familywise error rate (k-FWER) under arbitrary variable dependence, substantially reducing researcher degrees of freedom. Third, it integrates machine learning interpretability techniques with selective inference to ensure statistical validity. The method is empirically validated in economic analyses—both causal and descriptive—and is accompanied by an open-source Jupyter toolkit, enabling low-cost, fully reproducible empirical workflows.
📝 Abstract
Social scientists are increasingly turning to unstructured datasets to unlock new empirical insights, e.g., estimating causal effects on text outcomes, measuring beliefs from open-ended survey responses. In such settings, unsupervised analysis is often of interest, in that the researcher does not want to pre-specify the objects of measurement or otherwise artificially delimit the space of measurable concepts; they are interested in discovery. This paper proposes a general and flexible framework for pursuing discovery from unstructured data in a statistically principled way. The framework leverages recent methods from the literature on machine learning interpretability to map unstructured data points to high-dimensional, sparse, and interpretable dictionaries of concepts; computes (test) statistics of these dictionary entries; and then performs selective inference on them using newly developed statistical procedures for high-dimensional exceedance control of the $k$-FWER under arbitrary dependence. The proposed framework has few researcher degrees of freedom, is fully replicable, and is cheap to implement -- both in terms of financial cost and researcher time. Applications to recent descriptive and causal analyses of unstructured data in empirical economics are explored. An open source Jupyter notebook is provided for researchers to implement the framework in their own projects.