Causal discovery aims to identify causal relationships from observational data. Recently, optimization-based causal discovery methods have attracted extensive attention in the literature due to their efficiency in handling high-dimensional problems. However, we observe that optimization-based methods often perform well on certain problems but struggle with others. This paper identifies a specific characteristic of causal structural equations that determines the difficulty of identification in causal discovery and, in turn, the performance of optimization-based methods. We conduct an in-depth study of the additive noise model (ANM) and propose to further divide identifiable problems into strongly and weakly identifiable types based on the difficulty of identification. We also provide a sufficient condition to distinguish the two categories. Inspired by these findings, this paper further proposes GENE, a generic method for addressing strongly and weakly identifiable problems in a unified way under the ANM assumption. GENE adopts an order-based search framework that incorporates conditional independence tests into order fitness evaluation, ensuring effectiveness on weakly identifiable problems. In addition, GENE restricts the dimensionality of the effect variables to ensure scale invariance, a property crucial for practical applications. Experiments demonstrate that GENE is uniquely effective in addressing weakly identifiable problems while also remaining competitive with state-of-the-art causal discovery algorithms for strongly identifiable problems.

Computerized Adaptive Testing (CAT) is a testing technique that accurately infers students' proficiency levels using a relatively small number of questions.Most existing CAT systems operate on a question-level adaptive paradigm, which is suitable for practice scenarios.However, in computerized standardized high-stakes examinations such as the GRE and GMAT, this paradigm faces several challenges: (1) the lack of comparability in exam results, (2) high implementation costs due to the reliance on real-time interactions and the financial burden of maintaining CAT testing system, and (3) the difficulty in balancing multiple factors of diagnosis quality, attribute coverage, and question exposure.To address these challenges, we propose a Paper-level Computerized Adaptive Testing (PCAT) and its corresponding evaluation method.PCAT divides an exam into multiple testing stages, where examinees adaptively receive test papers of varying difficulty based on their performance in previous stages.The paper assembly problem in PCAT is solved using a population-based multi-objective optimization (MOO) approach.PCAT offers several advantages: First, the paper-level adaptive mechanism ensures that the questions faced by examinees depend solely on their performance in the earlier stages, maintaining adaptability while enhancing the comparability of results across different examinees.Second, PCAT replaces the selection strategy module in traditional CAT with an assembly module, allowing computationally intensive tasks such as cognitive diagnosis and paper assembly to be completed offline before the exam, eliminating the need for real-time interactions.Additionally, the population-based MOO approach generates a set of high-quality solutions in one run, meeting the demands of frequent administration of standardized high-stakes exams like the GRE and reducing the financial burden of maintaining a large-scale CAT system.Finally, MOO naturally

Causal global optimization (CGO) aims to complete optimization tasks through causal inference. In the high-dimensional CGO problems, traditional causal Bayesian optimization (CBO) methods struggle with the curse of dimensionality attributed to the number of variables in the causal graph, and scale inconsistency among Gaussian Process (GP) models. These issues limit the application of CBO in domains requiring optimization over large causal graphs. To address these limitations, this paper proposes a high-dimensional causal Bayesian optimization (HCBO) algorithm. To address the curse of dimensionality, HCBO introduces a submodularity indicator for variable subsets through the concept of causal intrinsic dimensionality (CID). It then uses the submodular optimization algorithm to find approximations of CID within polynomial sample complexity. Theoretically, we disclose a sufficient condition for CID’s existence. To address the issue of scale inconsistency among GP models, HCBO introduces a scale-normalized scoring function, ensuring stable identification of the optimal GP model corresponding to CID for intervention. Extensive experiments are conducted on high-dimensional synthetic and real-world tasks, i.e., coral ecology and health. The existence of CID is verified across the datasets of all tasks. HCBO achieves state-of-the-art performance in CGO problems and can handle causal graphs at a scale 10 times larger than that manageable by previous CBO methods.

Cognitive diagnosis models (CDMs) are designed to learn students' mastery levels using their response logs.CDMs play a fundamental role in online education systems since they significantly influence downstream applications such as teachers' guidance and computerized adaptive testing.Despite the success achieved by existing CDMs, we find that they suffer from a thorny issue that the learned students' mastery levels are too similar.This issue, which we refer to as oversmoothing, could diminish the CDMs' effectiveness in downstream tasks.CDMs comprise two core parts: learning students' mastery levels and assessing mastery levels by fitting the response logs.This paper contends that the oversmoothing issue arises from that existing CDMs seldom utilize response signals on exercises in the learning part but only use them as labels in the assessing part.To this end, this paper proposes an oversmoothing-resistant cognitive diagnosis framework (ORCDF) to enhance existing CDMs by utilizing response signals in the learning part.Specifically, OR-CDF introduces a novel response graph to inherently incorporate response signals as types of edges.Then, ORCDF designs a tailored response-aware graph convolution network (RGC) that effectively captures the crucial response signals within the response graph.Via ORCDF, existing CDMs are enhanced by replacing the input embeddings with the outcome of RGC, allowing for the consideration of response signals on exercises in the learning part.Extensive experiments on real-world datasets show that ORCDF not only *

In modern telecommunication networks, faults manifest as alarms, generating thousands of events daily. Network operators need an efficient method to identify the root causes of these alarms to mitigate potential losses. This task is challenging due to the increasing scale of telecommunication networks and the interconnected nature of devices, where one fault can trigger a cascade of alarms across multiple devices within a topological network. Recent years have seen a growing focus on causal approaches to addressing this problem, emphasizing the importance of learning a Granger causal graph from topological event sequences. Such causal graphs delineate the relations among alarms and can significantly aid engineers in identifying and rectifying faults. However, existing methods either ignore the topological relationships among devices or suffer from relatively low scalability and efficiency, failing to deliver high-quality responses in a timely manner. To this end, this paper proposes $S^2GCSL$, a simple yet scalable Granger causal structural learning approach for topological event sequences. $S^2GCSL$ utilizes a linear kernel to model activation interactions among various event types within a topological network, and employs gradient descent to efficiently optimize the likelihood function. Notably, it can seamlessly incorporate expert knowledge as constraints within the optimization process, which enhances the interpretability of the outcomes. Extensive experimental results on both large-scale synthetic and real-world problems verify the scalability and efficacy of $S^2GCSL$.

Cognitive diagnosis is vital for intelligent education to determine students’ knowledge mastery levels from their response logs. The Q-matrix, representing the relationships between exercises and knowledge attributes, improves the interpretability of cognitive diagnosis models. However, completing the Q-matrix poses an expensive and challenging task due to the fine-grained division of knowledge attributes. Moreover, a manually sparse Q-matrix can also compromise the accuracy and interpretability of deducing students’ mastery levels, especially for infrequently observed or unseen knowledge attributes. To address this issue, this paper proposes a Q-augmented Causal Cognitive Diagnosis Model (QCCDM) for student learning. Specifically, QCCDM incorporates the structure causal model (SCM) to capture the causality between students’ mastery levels on different attributes, which enables to infer their proficiency on rarely observed knowledge attributes with better accuracy and interpretability. Notably, with SCM, one can guide students on how to realize their self-improvement through intervention. Furthermore, we propose to augment the Q-matrix in QCCDM, which uses the manual Q-matrix as a prior to deduce the relationships between exercises and explicit as well as latent knowledge attributes, resulting in a complete and comprehensive assessment of students’ abilities. We assess the efficacy of Q-augmentation across the widely-used Q-based cognitive diagnosis models and conduct the ablation study. The extensive experimental results on real-world datasets show that QCCDM outperforms the compared methods in terms of both accuracy and interpretability.