  Despite the excelling performance of machine learning models, understanding
the decisions of machine learning models remains a long-standing goal. While
commonly used attribution methods in explainable AI attempt to address this
issue, they typically rely on associational rather than causal relationships.
In this study, within the context of time series classification, we introduce a
novel framework to assess the causal effect of concepts, i.e., predefined
segments within a time series, on specific classification outcomes. To achieve
this, we leverage state-of-the-art diffusion-based generative models to
estimate counterfactual outcomes. Our approach compares these causal
attributions with closely related associational attributions, both
theoretically and empirically. We demonstrate the insights gained by our
approach for a diverse set of qualitatively different time series
classification tasks. Although causal and associational attributions might
often share some similarities, in all cases they differ in important details,
underscoring the risks associated with drawing causal conclusions from
associational data alone. We believe that the proposed approach is widely
applicable also in other domains, particularly where predefined segmentations
are available, to shed some light on the limits of associational attributions.
