Testing Causal Theories with Learned Proxies

Social scientists commonly use computational models to estimate proxies of unobserved concepts, then incorporate these proxies into subsequent tests of their theories. The consequences of this practice, which occurs in over two-thirds of recent computational work in political science, are underappreciated. Imperfect proxies can reflect noise and contamination from other concepts, producing biased point estimates and standard errors. We demonstrate how analysts can use causal diagrams to articulate theoretical concepts and their relationships to estimated proxies, then apply straightforward rules to assess which conclusions are rigorously supportable. We formalize and extend common heuristics for "signing the bias"-a technique for reasoning about unobserved confounding-to scenarios with imperfect proxies. Using these tools, we demonstrate how, in often-encountered research settings, proxy-based analyses allow for valid tests for the existence and direction of theorized effects. We conclude with best-practice recommendations for the rapidly growing literature using learned proxies to test causal theories.