Statistical Policy Comparison (SPC) assesses the equivalence of two stochastic policies (policy consistency) and has received broad attention. However, the SPC framework implicitly assumes the invariance of decision environments, and therefore fails to address a flurry of real-world data science applications. In this work, we refer to this overlooked issue as environment consistency, and together with policy consistency, this extends to a generalized concept process consistency for systematically comparing policy trials under the Markov decision process (MDP) framework. To address process consistency, we propose a unified comparison framework, extending beyond traditional statistical policy comparison studies by incorporating both policy and environment comparisons. For policy consistency, existing statistical policy comparison methods can be seamlessly integrated into our intentionally-designed framework without modification. Specifically for environment consistency (the focus of this work), we devise fine-grained return tests to capture shifts of key elements in MDPs; notably, under special cases where trajectory likelihood information is available or can be estimated, we introduce a trajectory test based on the likelihood ratio test (LRT), offering increased testing power. Extensive experiments demonstrate that our proposed testing methods achieve higher statistical power than existing approaches in testing process consistency, establishing their effectiveness across diverse real-world scenarios. Our code is available at https://github.com/bcxyf123/MDP-Testing.git.

Despite significant advancements in causal research on graphs and its application to cracking label imbalance, the role of edge features in detecting the causal effects within graphs has been largely overlooked, leaving existing methods with untapped potential for further performance gains. In this paper, we enhance the causal attention mechanism through effectively leveraging edge information to disentangle the causal subgraph from the original graph, as well as further utilizing edge features to reshape graph representations. Capturing more comprehensive causal signals, our design leads to improved performance on graph classification tasks with label imbalance issues. We evaluate our approach on real-word datasets PTC, Tox21, and ogbg-molhiv, observing improvements over baselines. Overall, we highlight the importance of edge features in graph causal detection and provide a promising direction for addressing label imbalance challenges in graph-level tasks. The preprint version is available at https://arxiv.org/abs/2501.01707. The model implementation details and the codes are available on https://github.com/fengrui-z/ECAL.

Through recognizing causal subgraphs, causal graph learning (CGL) has risen to be a promising approach for improving the generalizability of graph neural networks under out-of-distribution (OOD) scenarios. However, the empirical successes of CGL techniques are mostly exemplified in classification settings, while regression tasks, a more challenging setting in graph learning, are overlooked. We thus devote this work to tackling causal graph regression (CGR); to this end we reshape the processing of confounding effects in existing CGL studies, which mainly deal with classification. Specifically, we reflect on the predictive power of confounders in graph-level regression, and generalize classification-specific causal intervention techniques to regression through a lens of contrastive learning. Extensive experiments on graph OOD benchmarks validate the efficacy of our proposals for CGR. The model implementation and the code are provided on https://github.com/causal-graph/CGR.

The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signals. Recently, a novel multi-compartment spiking neuron model, namely the Two-Compartment LIF (TC-LIF) model, has been proposed and exhibited a remarkable capacity for sequential modelling. However, training the TC-LIF model presents challenges stemming from the large memory consumption and the issue of vanishing gradient associated with the Backpropagation Through Time (BPTT) algorithm. To address these challenges, online learning methodologies emerge as a promising solution. Yet, to date, the application of online learning methods in SNNs has been predominantly confined to simplified Leaky Integrate-and-Fire (LIF) neuron models. In this paper, we present a novel online learning method specifically tailored for networks of TC-LIF neurons. Additionally, we propose a refined TC-LIF neuron model called Adaptive TC-LIF, which is carefully designed to enhance temporal information integration in online learning scenarios. Extensive experiments, conducted on various sequential benchmarks, demonstrate that our approach successfully preserves the superior sequential modeling capabilities of the TC-LIF neuron while incorporating the training efficiency and hardware friendliness of online learning. As a result, it offers a multitude of opportunities to leverage neuromorphic solutions for processing temporal signals.