The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Causal Interpretations of Black-Box Models
Open accessAbstract
The fields of machine learning and causal inference have developed many concepts, tools, and theory that are potentially useful for each other. Through exploring the possibility of extracting causal interpretations from black-box machine-trained models, we briefly review the languages and concepts in causal inference that may be interesting to machine learning researchers. We start with the curious observation that Friedman's partial dependence plot has exactly the same formula as Pearl's back-door adjustment and discuss three requirements to make causal interpretations: a model with good predictive performance, some domain knowledge in the form of a causal diagram and suitable visualization tools. We provide several illustrative examples and find some interesting and potentially causal relations using visualization tools for black-box models.
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Research works printed but not linked: 3
- Hernan, M. A. , and Robins, J. M . (2019), Causal Inference , Boca Raton, FL: Chapman & Hall/CRC (forthcoming).
- Jiang, T. , and Owen, A. B . (2002), “Quasi-Regression for Visualization and Interpretation of Black Box Functions,” Technical Report, Stanford University, Stanford.
- Zhao Q . (2016) “Topics in Causal and High Dimensional Inference ” PhD thesis Stanford University.
Cited by
Random forests for global sensitivity analysis: A selective review
Antoniadis, A., Lambert-Lacroix, S., & Poggi, J.-M. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107312 · Cited by 363
Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.003 · Cited by 302
Nazir, S., Dickson, D. M., & Akram, M. U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106668 · Cited by 283
“The Partial Dependence Plot (PDP) is a popular tool for visualizing black box models and can highlight the feature importance [67].”
MethodMoustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3280465 · Cited by 266
Four principles of explainable artificial intelligence
Phillips, P. J., Hahn, C. A., Fontana, P. C., Yates, A. N., Greene, K., Broniatowski, D. A., & Przybocki, M. A. (2021) · doi:10.6028/nist.ir.8312 · Cited by 262
Metadata
- Authors
- Zhao, Q., & Hastie, T.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2019-07-05
- Volume
- 39
- Issue
- 1
- Pages
- 272-281
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2948056727
- Cited by
- 462
- References
- 59
- FWCI
- 39.31
- Type
- journal article
- Language
- en
- Open access
- green
- Funders
- National Institutes of Health (5R01 EB 001988-21)
- National Science Foundation (DMS-1407548)
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