Connor Lennon, Edward Rubin, Glen Waddell
arXiv 19 May 2025 · Econometrics
arXiv:2505.13422 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Angrist, J. D. and Frandsen, B (2022) Machine Labor | 1.000 | 6 | 4 | 100% |
| 2 | Belloni, A., Chen, D., Chernozhukov, V., and Hansen, C (2012) Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain | 0.977 | 15 | 5 | 93% |
| 3 | Chen, J., Chen, D. L., and Lewis, G (2020) Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models | 0.971 | 12 | 5 | 92% |
| 4 | Bennett, A., Kallus, N., and Schnabel, T (2019) Deep generalized method of moments for instrumental variable analysis | 0.737 | 3 | 3 | 67% |
| 5 | Hartford, J., Lewis, G., Leyton-Brown, K., and Taddy, M (2017) Deep IV: A Flexible Approach for Counterfactual Prediction | 0.737 | 3 | 3 | 67% |
| 6 | Angrist, J. D. and Krueger, A. B (2001) Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments | 0.644 | 2 | 2 | 100% |
| 7 | Singh, R., Sahani, M., and Gretton, A (2019) Kernel instrumental variable regression | 0.644 | 2 | 2 | 100% |
| 8 | Angrist, J. and Pischke, J.-S (2009) Mostly Harmless Econometrics: An Empiricist's Companion | 0.511 | 2 | 2 | 50% |
| 9 | Belloni, A., Chernozhukov, V., and Hansen, C (2013) Inference on Treatment Effects after Selection among High-Dimensional Controls | 0.511 | 2 | 2 | 50% |
| 10 | Kilbertus, N., Kusner, M. J., and Silva, R (2020) A class of algorithms for general instrumental variable models | 0.511 | 2 | 2 | 50% |
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