Jiafeng Chen, Daniel L. Chen, Greg Lewis
arXiv 12 Nov 2020 · Econometrics · 7 citations (OpenAlex)
arXiv:2011.06158 · PDF · DOI · OpenAlex · Extracted main text
We offer straightforward theoretical results that justify incorporating machine learning in the standard linear instrumental variable setting. The key idea is to use machine learning, combined with sample-splitting, to predict the treatment variable from the instrument and any exogenous covariates, and then use this predicted treatment and the covariates as technical instruments to recover the coefficients in the second-stage. This allows the researcher to extract non-linear co-variation between the treatment and instrument that may dramatically improve estimation precision and robustness by boosting instrument strength. Importantly, we constrain the machine-learned predictions to be linear in the exogenous covariates, thus avoiding spurious identification arising from non-linear relationships between the treatment and the covariates. We show that this approach delivers consistent and asymptotically normal estimates under weak conditions and that it may be adapted to be semiparametrically efficient (Chamberlain, 1992). Our method preserves standard intuitions and interpretations of linear instrumental variable methods, including under weak identification, and provides a simple, user-friendly upgrade to the applied economics toolbox. We illustrate our method with an example in law and criminal justice, examining the causal effect of appellate court reversals on district court sentencing decisions.
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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 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.874 | 6 | 2 | 100% |
| 2 | –- (1992) Comment: Sequential moment restrictions in panel data | 0.843 | 3 | 3 | 100% |
| 3 | Chamberlain, G (1987) Asymptotic efficiency in estimation with conditional moment restrictions | 0.811 | 4 | 2 | 100% |
| 4 | Andrews, I., Stock, J. H. and Sun, L (2019) Weak instruments in iv regression: Theory and practice | 0.737 | 3 | 2 | 100% |
| 5 | –- and Pischke, J.-S (2008) Mostly harmless econometrics: An empiricist's companion | 0.737 | 3 | 2 | 100% |
| 6 | Angrist, J. and Frandsen, B (2019) Machine labor | 0.737 | 3 | 2 | 100% |
| 7 | Belloni, A., Chen, D., Chernozhukov, V. and Hansen, C (2012) Sparse models and methods for optimal instruments with an application to eminent domain self | 0.737 | 3 | 2 | 100% |
| 8 | Ai, C. and Chen, X (2003) Efficient estimation of models with conditional moment restrictions containing unknown functions | 0.644 | 2 | 2 | 100% |
| 9 | –- and –- (2007) Estimation of possibly misspecified semiparametric conditional moment restriction models with different conditioning variables | 0.644 | 2 | 2 | 100% |
| 10 | –- and –- (2012) The semiparametric efficiency bound for models of sequential moment restrictions containing unknown functions | 0.644 | 2 | 2 | 100% |
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