arXiv 24 Mar 2019 · Econometrics · publishedAnnual Review of Economics (2019) · 1,060 citations (OpenAlex)
arXiv:1903.10075 · PDF · DOI · OpenAlex · Extracted main text
We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the machine learning literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, as well as matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics, methods that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, problems that include causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | Stefan Wager and Susan Athey (2017) Estimation and inference of heterogeneous treatment effects using random forests self | 1.000 | 7 | 4 | 100% |
| 2 | Susan Athey and Guido Imbens (2016) Recursive partitioning for heterogeneous causal effects self | 1.000 | 5 | 3 | 100% |
| 3 | Susan Athey, Julie Tibshirani, and Stefan Wager Generalized random forests self | 0.928 | 4 | 3 | 100% |
| 4 | Max H Farrell, Tengyuan Liang, and Sanjog Misra (2018) Deep neural networks for estimation and inference: Application to causal effects and other semiparametric estimands | 0.928 | 4 | 3 | 100% |
| 5 | Rina Friedberg, Julie Tibshirani, Susan Athey, and Stefan Wager (2018) Local linear forests self | 0.811 | 4 | 2 | 100% |
| 6 | Sendhil Mullainathan and Jann Spiess (2017) Machine learning: an applied econometric approach | 0.811 | 4 | 2 | 100% |
| 7 | Francisco JR Ruiz, Susan Athey, and David M Blei (2017) Shopper: A probabilistic model of consumer choice with substitutes and complements self | 0.811 | 4 | 2 | 100% |
| 8 | Alexandre Belloni, Victor Chernozhukov, and Christian Hansen (2014) High-dimensional methods and inference on structural and treatment effects | 0.737 | 3 | 2 | 100% |
| 9 | Dimitris Bertsimas, Angela King, Rahul Mazumder, et al (2016) Best subset selection via a modern optimization lens | 0.737 | 3 | 2 | 100% |
| 10 | Léon Bottou (2012) Stochastic gradient descent tricks | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 144 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.