Evaluating Explanation Methods of Multivariate Time Series Classification through Causal Lenses

Explainable machine learning techniques (XAI) aim to provide a solid descriptive approach to Deep Neural Networks (NN). In Multi-Variate Time Series (MTS) analysis, the most recurrent techniques use relevance attribution, where importance scores are assigned to each TS variable over time according to their importance in classification or forecasting. Despite their popularity, post-hoc explanation methods do not account for causal relationships between the model outcome and its predictors. In our work, we conduct a thorough empirical evaluation of model-agnostic and model-specific relevance attribution methods proposed for TCNN, LSTM, and Transformers classification models of MTS. The contribution of our empirical study is threefold: (i) evaluate the capability of existing post-hoc methods to provide consistent explanations for high-dimensional MTS (ii) quantify how post-hoc explanations are related to sufficient explanations (i.e., the direct causes of the target TS variable) underlying the datasets, and (iii) rank the performance of surrogate models built over post-hoc and causal explanations w.r.t. the full MTS models. To the best of our knowledge, this is the first work that evaluates the reliability and effectiveness of existing XAI methods from a temporal causal model perspective.