Mining Causality: AI-Assisted Search for Instrumental Variables

The instrumental variables (IVs) method is a leading empirical strategy for causal inference. Finding IVs is a heuristic and creative process, and justifying its validity - especially exclusion restrictions - is largely rhetorical. We propose using large language models (LLMs) to search for new IVs through narratives and counterfactual reasoning, similar to how a human researcher would. The stark difference, however, is that LLMs can dramatically accelerate this process and explore an extremely large search space. We demonstrate how to construct prompts to search for potentially valid IVs. We contend that multi-step and role-playing prompting strategies are effective for simulating the endogenous decision-making processes of economic agents or social actors and for navigating language models through the realm of real-world scenarios. We apply our method to four well-known examples in economics: returns to schooling, demand and supply, and peer effects. Expert surveys reveal that some of the discovered IVs in each domain appear both novel and likely valid.