Machine learning the first stage in 2SLS: Practical guidance from bias decomposition and simulation

Connor Lennon; Edward Rubin; Glen Waddell

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arXiv · 2505.13422

Machine learning the first stage in 2SLS: Practical guidance from bias decomposition and simulation

Connor Lennon·Edward Rubin·Glen Waddell

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Abstract

Machine learning (ML) primarily evolved to solve "prediction problems." The first stage of two-stage least squares (2SLS) is a prediction problem, suggesting potential gains from ML first-stage assistance. However, little guidance exists on when ML helps 2SLS$\unicode{x2014}$or when it hurts. We investigate the implications of inserting ML into 2SLS, decomposing the bias into three informative components. Mechanically, ML-in-2SLS procedures face issues common to prediction and causal-inference settings$\unicode{x2014}$and their interaction. Through simulation, we show linear ML methods (e.g., post-Lasso) work well, while nonlinear methods (e.g., random forests, neural nets) generate substantial bias in second-stage estimates$\unicode{x2014}$potentially exceeding the bias of endogenous OLS.

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econ.EM cs.LG stat.AP stat.ML

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Connor Lennon, Edward Rubin, Glen Waddell. 2025-05-19. Machine learning the first stage in 2SLS: Practical guidance from bias decomposition and simulation. https://arxiv.org/abs/2505.13422

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