Approaching historical data collection with causal inference in mind

I argue we can be more systematic about approaching and collecting historical data with the intention of using it for quantitative causal inference (CI). I discuss common challenges to be aware of when working with historical data, how to better structure visits to libraries or archives, and how innovations in research transparency and research design—namely, preanalysis plans for observational data—can be used as tools to help improve our collection of historical data. I emphasize that scholars should spend more effort at the research design stage in order to understand when and why data are available, and what biases might be present. Recognizing what historical data is needed for causal inference, and acknowledging what is not available, can help the research in the long term.