Causal Interpretability for Machine Learning – Problems, Methods and Evaluation
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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}