Yucheng Yang, Zhong Zheng, Weinan E
arXiv 11 Oct 2020 · Econometrics
arXiv:2010.05311 · PDF · Extracted main text
The lack of interpretability and transparency are preventing economists from using advanced tools like neural networks in their empirical research. In this paper, we propose a class of interpretable neural network models that can achieve both high prediction accuracy and interpretability. The model can be written as a simple function of a regularized number of interpretable features, which are outcomes of interpretable functions encoded in the neural network. Researchers can design different forms of interpretable functions based on the nature of their tasks. In particular, we encode a class of interpretable functions named persistent change filters in the neural network to study time series cross-sectional data. We apply the model to predicting individual's monthly employment status using high-dimensional administrative data. We achieve an accuracy of 94.5% in the test set, which is comparable to the best performed conventional machine learning methods. Furthermore, the interpretability of the model allows us to understand the mechanism that underlies the prediction: an individual's employment status is closely related to whether she pays different types of insurances. Our work is a useful step towards overcoming the black-box problem of neural networks, and provide a new tool for economists to study administrative and proprietary big data.
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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 | W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and B… (2019) Definitions, methods, and applications in interpretable machine learning | 0.928 | 4 | 3 | 100% |
| 2 | Shuaizhang Feng, Yingyao Hu, and Robert Moffitt (2017) Long run trends in unemployment and labor force participation in urban China | 0.644 | 2 | 2 | 100% |
| 3 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters, 2018 | 0.511 | 2 | 1 | 100% |
| 4 | Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and… (2018) Human decisions and machine predictions | 0.511 | 2 | 1 | 100% |
| 5 | Sendhil Mullainathan and Jann Spiess (2017) Machine learning: an applied econometric approach | 0.511 | 2 | 1 | 100% |
| 6 | Alberto Abadie (2019) Using synthetic controls: Feasibility, data requirements, and methodological aspects | 0.405 | 1 | 1 | 100% |
| 7 | Joshua D Angrist and Jörn-Steffen Pischke (2008) Mostly harmless econometrics: An empiricist's companion | 0.405 | 1 | 1 | 100% |
| 8 | Susan Athey (2018) The impact of machine learning on economics | 0.405 | 1 | 1 | 100% |
| 9 | Susan Athey, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and Kh… (2018) Matrix completion methods for causal panel data models | 0.405 | 1 | 1 | 100% |
| 10 | Alessandro Casini and Pierre Perron (2018) Structural breaks in time series | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 25 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | HMM-LSTM Fusion Model for Economic Forecasting | 0.511 | 2 | 1 |