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We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such).
Recursive causal models
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Causality
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Searching for higgs boson decay modes with deep learning
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Zeiler, M. D. and Fergus, R · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
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Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
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Dheeru, D. and Karra Taniskidou, E · 2017
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Distilling a neural network into a soft decision tree
Frosst, N. and Hinton, G · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Causalgan: Learning causal implicit generative models with adversarial training
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Zhou, J. and Troyanskaya, O. G · 2015
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Tensorflow: A system for large-scale machine learning
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Bongers, S., Peters, J., Schölkopf, B., and Mooij, J. M · 2016
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Christopher, M. B · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2016
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Why should i trust you?: Explaining the predictions of any classifier
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Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., and Müller, K.-R · 2017
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Peters, J., Janzing, D., and Schölkopf, B · 2017
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Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
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Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Towards robust interpretability with self-explaining neural networks
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Li, O., Liu, H., Chen, C., and Rudin, C · 2018
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