Mochen Yang, Edward McFowland III, Gordon Burtch, Gediminas Adomavicius
arXiv 19 Dec 2020 · Econometrics · publishedINFORMS Journal on Data Science (2022) · 8 citations (OpenAlex)
arXiv:2012.10790 · PDF · DOI · OpenAlex · Extracted main text
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to 'mine' variables of interest from available data, followed by the inclusion of those variables into an econometric framework, with the objective of estimating causal effects. Recent work highlights that, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables are likely to suffer from bias due to measurement error. We propose a novel approach to mitigate these biases, leveraging the ensemble learning technique known as the random forest. We propose employing random forest not just for prediction, but also for generating instrumental variables to address the measurement error embedded in the prediction. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make 'different' mistakes, i.e., have weakly correlated prediction errors. A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the other trees serve as its instruments. Simulation experiments demonstrate the efficacy of the proposed approach in mitigating estimation biases and its superior performance over three alternative methods for bias correction.
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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 | Scornet, E., Biau, G., Vert, J.-P., et al (2015) Consistency of random forests | 0.874 | 6 | 3 | 67% |
| 2 | Bernard, S., Heutte, L., and Adam, S (2010) A study of strength and correlation in random forests | 0.737 | 3 | 3 | 67% |
| 3 | Greene, W. H (2003) Econometric analysis | 0.737 | 3 | 3 | 67% |
| 4 | Breiman, L (2001) Random forests | 0.693 | 9 | 4 | 33% |
| 5 | Yang, M., Adomavicius, G., Burtch, G., and Ren, Y (2018) Mind the gap: Accounting for measurement error and misclassification in variables generated via data mining self | 0.693 | 9 | 3 | 33% |
| 6 | Meng, L., Wu, B., and Zhan, Z (2016) Linear regression with an estimated regressor: applications to aggregate indicators of economic development | 0.585 | 10 | 3 | 20% |
| 7 | Belloni, A., Chen, D., Chernozhukov, V., and Hansen, C (2012) Sparse models and methods for optimal instruments with an application to eminent domain | 0.585 | 3 | 1 | 100% |
| 8 | Cook, J. and Stefanski, L (1994) Simulation-extrapolation estimation in parametric measurement error models | 0.511 | 3 | 2 | 33% |
| 9 | Angrist, J. D. and Pischke, J.-S (2008) Mostly harmless econometrics: An empiricist's companion | 0.511 | 2 | 2 | 50% |
| 10 | Conley, T. G., Hansen, C. B., and Rossi, P. E (2012) Plausibly exogenous | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 71 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference | 1.000 | 10 | 4 |