%0 Journal Article %1 zhao2019causal %A Zhao, Qingyuan %A Hastie, Trevor %D 2019 %I Taylor & Francis %J Journal of Business & Economic Statistics %K machine_learning prediction statistics %N 0 %P 1-10 %R 10.1080/07350015.2019.1624293 %T Causal Interpretations of Black-Box Models %U /brokenurl# https://doi.org/10.1080/07350015.2019.1624293 %V 0 %X 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.

@article{zhao2019causal, abstract = { 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. }, added-at = {2020-03-04T00:29:52.000+0100}, author = {Zhao, Qingyuan and Hastie, Trevor}, biburl = {https://www.bibsonomy.org/bibtex/283d317bcbda7d2f0fdc5db9ae61fff96/peter.ralph}, doi = {10.1080/07350015.2019.1624293}, eprint = {https://doi.org/10.1080/07350015.2019.1624293}, interhash = {91db20ba4e62119e8e8ba982f4bb4c9f}, intrahash = {83d317bcbda7d2f0fdc5db9ae61fff96}, journal = {Journal of Business \& Economic Statistics}, keywords = {machine_learning prediction statistics}, number = 0, pages = {1-10}, publisher = {Taylor & Francis}, timestamp = {2020-03-04T00:29:52.000+0100}, title = {Causal Interpretations of Black-Box Models}, url = {/brokenurl# https://doi.org/10.1080/07350015.2019.1624293 }, volume = 0, year = 2019 }