Mining Causality: AI-Assisted Search for Instrumental Variables

Sukjin Han

Papers from arXiv.org

Abstract: The instrumental variables (IVs) method is a leading empirical strategy for causal inference. Finding IVs is a heuristic and creative process, and justifying their 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 introduce a discovery pipeline that searches for potentially novel and valid IVs using two-step and role-playing prompting strategies. We contend that these strategies simulate the endogenous decision-making of economic agents and social actors and ground language models in real-world scenarios, thereby masking the IV discovery task itself. We apply our method to three canonical areas in economics: returns to schooling, demand and supply, and peer effects. We then introduce an evaluation pipeline in which we conduct expert surveys and train an LLM judge on the survey data. This evaluation reveals that the IV candidates discovered by our method appear both novel and likely valid, whereas a more direct search tends to return instruments already established in the literature.

Date: 2024-09, Revised 2026-09
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-ecm
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