Causal Interpretability for Machine Learning – Problems, Methods and Evaluation
Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu
SIGKDD Explorations Newsletter 22(1), pages 18–33, 16 pages, June 2020
Association for Computing Machinery (ACM), New York, NY, USA
@article{causalinterpretability-kddnews22,
address = {New York, NY, USA},
author = {Moraffah, Raha and Karami, Mansooreh and Guo, Ruocheng and Raglin, Adrienne and Liu, Huan},
doi = {10.1145/3400051.3400058},
issn = {1931-0145},
journal = {SIGKDD Explorations Newsletter},
keywords = {counterfactuals, interpratablity, causal inference, explainability, machine learning},
month = jun,
number = 1,
numpages = 16,
pages = {18--33},
publisher = {Association for Computing Machinery},
title = {Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation},
url = {https://doi.org/10.1145/3400051.3400058},
volume = 22,
year = 2020}
address = {New York, NY, USA},
author = {Moraffah, Raha and Karami, Mansooreh and Guo, Ruocheng and Raglin, Adrienne and Liu, Huan},
doi = {10.1145/3400051.3400058},
issn = {1931-0145},
journal = {SIGKDD Explorations Newsletter},
keywords = {counterfactuals, interpratablity, causal inference, explainability, machine learning},
month = jun,
number = 1,
numpages = 16,
pages = {18--33},
publisher = {Association for Computing Machinery},
title = {Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation},
url = {https://doi.org/10.1145/3400051.3400058},
volume = 22,
year = 2020}