Recent rapid advancements of machine learning have greatly enhanced the accuracy of prediction models, but most models remain "black boxes", making prediction error diagnosis challenging, especially with outliers. This lack of transparency hinders trust and reliability in industrial applications. Heuristic attribution methods, while helpful, often fail to capture true causal relationships, leading to inaccurate error attributions. Various root-cause analysis methods have been developed using Shapley values, yet they typically require predefined causal graphs, limiting their applicability for prediction errors in machine learning models. To address these limitations, we introduce the Causal-Discovery-based Root-Cause Analysis (CD-RCA) method that estimates causal relationships between the prediction error and the explanatory variables, without needing a pre-defined causal graph. By simulating synthetic error data, CD-RCA can identify variable contributions to outliers in prediction errors by Shapley values. Extensive experiments show CD-RCA outperforms current heuristic attribution methods.

In this paper, we deal with the evaluation problem of "causes of effects" (CoE), which focuses on the likelihood that one event was the cause of another. To assess this likelihood, three types of probabilities of causation have been utilized: probability of necessity, probability of sufficiency, and probability of necessity and sufficiency. However, these usually cannot be estimated, even if "effects of causes" (EoC) is estimable from statistical data, regardless of how large the data is. To solve this problem, we propose novel identification conditions for CoE, using an intermediate variable together with covariate information. Additionally, we also propose a new method for estimating CoE that is applicable whenever they are identifiable through the proposed identification conditions.

The need for explainability in time-series classification models has been increasing. Counterfactual explanations recommend how to modify the features of an original instance so that the prediction by a given classifier flips to the desired class. Since features in the time series are temporally dependent, interpretability is improved by considering intervals where the counterfactual can deviate from the original instance. In this study, we propose a model-agnostic counterfactual generation method (CEI) that jointly learns these intervals and the counterfactual. Furthermore, CEI can generate a counterfactual tailored to the directly specified limited number of intervals. We mathematically formulate CEI as a continuous optimization and demonstrate its effectiveness on the UCR datasets.

Low visibility condition affects the safety and operation of road, sea, and air traffic, which causes disruption in transportation and sometimes leads to serious accidents. Therefore, forecasting of low visibility accurately is of utmost importance for transportation agencies and aviation authorities. The conventional approach of visibility forecasting using time-series weather data cannot forecast low visibility with high accuracy due to imbalanced and clipped visibility data used to build a forecasting model. To address this issue, we propose a new approach that combines forecasting and classification methods with discrete and kernel density estimation (KDE)-based weights to build forecasting models for multi-step ahead forecasting. We show the effectiveness of our proposed approach by performing experiments with four publicly available visibility datasets of weather and air quality data from the USA and Japan and evaluating in terms of forecasting errors and accuracy of forecasting of low visibility. Experimental results suggest that our proposed approach can improve RMSE and MAE in the low visibility range by respectively 29–54 % and 40–59 % and F-score of forecasting of low and high visibility by 0.20-0.26 on the average over existing technologies for 3–24 hour ahead forecasting, which implies that it is well-suited for forecasting of low visibility.

We propose novel identification conditions and a statistical estimation method for the probabilities of potential outcome types using covariate information in randomized trials in which the treatment assignment is randomized but subject compliance is not perfect. Different from existing studies, the proposed identification conditions do not require strict assumptions such as the assumption of monotonicity. When the probabilities of potential outcome types are identifiable through the proposed conditions, the problem of estimating the probabilities of potential outcome types is reduced to that of singular models. Thus, the probabilities cannot be evaluated using standard statistical likelihood-based estimation methods. Rather, the proposed identification conditions show that we can derive consistent estimators of the probabilities of potential outcome types via the method of moments, which leads to the asymptotic normality of the proposed estimators through the delta method under regular conditions. We also propose a new statistical estimation method based on the bounded constrained augmented Lagrangian method to derive more efficient estimators than can be derived through the method of moments.

The concept of potential outcome types is one of the fundamental components of causal inference. However, even in randomized experiments, assumptions on the data generating process, such as monotonicity, are required to evaluate the probabilities of the potential outcome types. To solve the problem without such assumptions in experimental studies, a novel identification condition based on proxy covariate information is proposed in this paper. In addition, the estimation problem of the probabilities of the potential outcome types reduces to that of singular models when they are identifiable through the proposed condition. Thus, they cannot be evaluated by standard statistical estimation methods. To overcome this difficulty, new plug-in estimators of these probabilities are presented, and the asymptotic normality of the proposed estimators is shown.

The joint probabilities of potential outcomes are fundamental components of causal inference in the sense that (i) if they are identifiable, then the causal risk is also identifiable, but not vise versa (Pearl, 2009; Tian and Pearl, 2000) and (ii) they enable us to evaluate the probabilistic aspects of necessity'',sufficiency'', and ``necessity and sufficiency'', which are important concepts of successful explanation (Watson, et al., 2020). However, because they are not identifiable without any assumptions, various assumptions have been utilized to evaluate the joint probabilities of potential outcomes, e.g., the assumption of monotonicity (Pearl, 2009; Tian and Pearl, 2000), the independence between potential outcomes (Robins and Richardson, 2011), the condition of gain equality (Li and Pearl, 2019), and the specific functional relationships between cause and effect (Pearl, 2009). Unlike existing identification conditions, in order to evaluate the joint probabilities of potential outcomes without such assumptions, this paper proposes two types of novel identification conditions using covariate information. In addition, when the joint probabilities of potential outcomes are identifiable through the proposed conditions, the estimation problem of the joint probabilities of potential outcomes reduces to that of singular models and thus they can not be evaluated by standard statistical estimation methods. To solve the problem, this paper proposes a new statistical estimation method based on the augmented Lagrangian method and shows the asymptotic normality of the proposed estimators. Given space constraints, the proofs, the details on the statistical estimation method, some numerical experiments, and the case study are provided in the supplementary material.