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What went wrong and when? Instance-wise feature importance for time-series black-box models
Sana Tonekaboni · Shalmali Joshi · Kieran Campbell · David Duvenaud · Anna Goldenberg

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1069

Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature. We propose FIT, a framework that evaluates the importance of observations for a multivariate time-series black-box model by quantifying the shift in the predictive distribution over time. FIT defines the importance of an observation based on its contribution to the distributional shift under a KL-divergence that contrasts the predictive distribution against a counterfactual where the rest of the features are unobserved. We also demonstrate the need to control for time-dependent distribution shifts. We compare with state-of-the-art baselines on simulated and real-world clinical data and demonstrate that our approach is superior in identifying important time points and observations throughout the time series.

Author Information

Sana Tonekaboni (University of Toronto / Vector Institute)
Shalmali Joshi (Harvard University (SEAS))
Kieran Campbell (University of British Columbia)
David Duvenaud (University of Toronto)
Anna Goldenberg

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