# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Causal and Temporal Dependency Tests
#### 6.4.1 Technical and Methodological Insights  
The pursuit of explainability in drift binds the Causal and Temporal Dependency Tests class, where [PERSON] et al. [40; 94] and [PERSON] et al. [114] collectively shift focus from detecting drift to interpreting it through temporally grounded causal chains. At its core, this class contends that meaningful understanding emerges not from identifying when a drift occurred, but from uncovering why it happened by rigorously mapping directional dependencies between events. [PERSON] et al. laid this foundation by integrating Granger-causality testing within a change-point detection framework, explicitly generating \"edge tables\" that translate statistical drift alerts into interpretable claims about which metric drives which. This transformed isolated drift points into relational explanations--yet remained constrained by its reliance on linear models, leaving potentially critical non-linear interactions unexplored and limiting explanatory depth in complex systems. Building directly on this limitation, [PERSON] et al. elevated explanatory power by introducing kernel-based causality testing, which reveals non-linear dependencies invisible to traditional methods. Crucially, they embedded these insights into an automated visual narrative--the \"causal ribbon\" matrix--that dynamically links validated cause-effect pairs across time. Color-coded paths (e.g., red to green) transform statistical abstractions (\(p\)-values, lags) into an intuitive story: for instance, a workload spike in week 20 non-linearly triggering a collapse in loan-validation service times five weeks later. This visual scaffolding allows domain experts like credit officers to immediately grasp why retraining for a sub-process is justified, bypassing statistical complexity. Furthermore, by leveraging object-centric event logs, the method preserves contextual interactions between entities, ensuring explanations reflect operational reality rather than flattening artifacts--a significant stride toward trustworthy interpretability. Demonstrating the versatility of this causality-first paradigm, [PERSON] et al. extended it beyond process mining to environmental drift. Applying kernel causality to exogenous variables, they attributed dissolved-oxygen drift to typhoon-induced wind patterns, with detected one-week lags providing mechanistic explanations for stratification changes. While sharing [PERSON] et al.'s commitment to causal attribution as the bedrock of interpretability, their approach prioritized statistical rigor over integrated visualization, illustrating how the explanatory framework adapts to diverse domains while emphasizing different facets of delivery. Across these works, a unified philosophy emerges: explainability is synonymous with empirically verified causal pathways anchored in time. All methods detect drift points and temporal lags, apply causality tests (Granger or kernel) as explanatory engines, and output actionable attributions (e.g., \"Metric A at \(t_{1}\)\(\rightarrow\) Metric B at \(t_{2}\)\"). Quantifiable confidence metrics (p-values, Bayes factors) underpin claims, but the frameworks consciously translate them into accessible formats--whether through [PERSON] et al.'s visual ribbons or [PERSON] et al.'s lag-based narratives. This evolution reveals a trajectory toward deeper, more actionable interpretability: [PERSON] et al. subsumes their earlier work by unifying non-linear discovery with visual storytelling, while [PERSON] et al. validate the paradigm's adaptability. Nevertheless, the class collectively focuses on metric-level drivers (e.g., workload spikes or wind speeds) rather than concept-level reasoning (e.g., why typhoons alter system behavior), leaving room for future work. Ultimately, their strength lies in rigorously answering \"why\" through causal chains, directly aligning drift explanations with domain-actionable insights. As such, the explanatory process is diagnostic and applied after observation, fitting this class under Explainable AI.