Mining Causality: AI-Assisted Search for Instrumental Variables

• URL: http://arxiv.org/abs/2409.14202v1
• Date: Sat, 21 Sep 2024 17:19:29 GMT
• Title: Mining Causality: AI-Assisted Search for Instrumental Variables
• Authors: Sukjin Han,
• Abstract summary: We propose using large language models to search for new IVs through narratives and counterfactual reasoning. We demonstrate how to construct prompts to search for potentially valid IVs. We apply our method to three well-known examples in economics: returns to schooling, production functions, and peer effects.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 accelerate this process exponentially and explore an extremely large search space. We demonstrate how to construct prompts to search for potentially valid IVs. We argue that multi-step prompting is useful and role-playing prompts are suitable for mimicking the endogenous decisions of economic agents. We apply our method to three well-known examples in economics: returns to schooling, production functions, and peer effects. We then extend our strategy to finding (i) control variables in regression and difference-in-differences and (ii) running variables in regression discontinuity designs.

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