The out-of-distribution (OOD) generalization of graph neural networks poses significant challenges in Web applications, where data resides in complex heterogeneous information networks. Such networks exhibit not only structural heterogeneity but also distribution shifts arising from evolving user behaviors and data collection biases. Conventional GNNs often struggle to identify stable patterns in such heterogeneous graphs, particularly in the absence of explicit environment labels. The core issue is that latent environments can create spurious correlations between node features, local topology, and labels. Models may then rely on these environment-specific shortcuts for predictions, failing to learn the invariant mechanisms that generalize under distribution shifts. To address these limitations, we propose InvHG (Invariant Learning on Heterogeneous Graphs via Subgraph Environment Inference), a causality-inspired framework that infers latent environments at the subgraph level, disentangles type-specific confounding effects, and leverages regularized expert fusion to learn invariant representations. Extensive experiments on heterogeneous graph OOD benchmarks demonstrate that InvHG consistently outperforms state-of-the-art methods, offering a robust solution for complex Web graph learning. The source code is available at https://github.com/mok630/InvHG.

To provide actionable insights and interpretations for individuals affected by algorithmic decisions, algorithmic recourse-demonstrating how outcomes change with modifications to input features-is introduced to facilitate outcome adjustment.However, existing studies often focus on different notions of feasibility and impose complex optimization constraints, relying on strong assumptions and expert knowledge that may be impractical or not widely applicable.In this paper, we propose leveraging adherence to the perturbation data manifold to model typical feasibility challenges, providing both a theoretical clarification and a practical framework.We design optimization constraints based on this model and introduce our method, the Perturbation Data Manifold Constraint (PDMC), to ensure the feasibility of generated algorithmic recourses.Through extensive experiments on both simulated and real clinical data, we validate the rationale and effectiveness of PDMC.

In this paper, we present the Proportional Payoff Allocation Game (PPA-Game), which characterizes situations where agents compete for divisible resources. In the PPA-game, agents select from available resources, and their payoffs are proportionately determined based on heterogeneous weights attributed to them. Such dynamics simulate content creators on online recommender systems like YouTube and TikTok, who compete for finite consumer attention, with content exposure reliant on inherent and distinct quality. We first conduct a game-theoretical analysis of the PPA-Game. While the PPA-Game does not always guarantee the existence of a pure Nash equilibrium (PNE), we identify prevalent scenarios ensuring its existence. Simulated experiments further prove that the cases where PNE does not exist rarely happen. Beyond analyzing static payoffs, we further discuss the agents' online learning about resource payoffs by integrating a multi-player multi-armed bandit framework. We propose an online algorithm facilitating each agent's maximization of cumulative payoffs over T rounds. Theoretically, we establish that the regret of any agent is bounded by O(log^1 + η T) for any η > 0. Empirical results further validate the effectiveness of our online learning approach.

Algorithmic recourse (AR) has made significant progress by identifying small perturbations in input features that can alter predictions, which provide a data-centric approach to understand decisions from diverse black-box models on the Web. Towards the feasibility issue, i.e., whether the recoursed examples provides actionable and reliable recommendations to end-users, causal algorithmic recourse have incorporated structural causal model (SCM) to preserve the realistic constraints among input features. For instance, preserving structural causal knowledge between "age" and "educational level" can avoid generating samples with decreasing age and increasing educational level. However, previous causal AR methods suffer from the requirement of prior structural causal knowledge, e.g., prior causal graph or the whole SCM, which restricts the realistic application of causal AR methods.

In the era of big data, decision-making in social networks may introduce bias due to interconnected individuals. For instance, in peer-to-peer loan platforms on the Web, considering an individual's attributes along with those of their interconnected neighbors, including sensitive attributes, is vital for loan approval or rejection downstream. Unfortunately, conventional fairness approaches often assume independent individuals, overlooking the impact of one person's sensitive attribute on others' decisions. To fill this gap, we introduce "Interference-aware Fairness" (IAF) by defining two forms of discrimination as Self-Fairness (SF) and Peer-Fairness (PF), leveraging advances in interference analysis within causal inference. Specifically, SF and PF causally capture and distinguish discrimination stemming from an individual's sensitive attributes (with fixed neighbors' sensitive attributes) and from neighbors' sensitive attributes (with fixed self's sensitive attributes), separately. Hence, a network-informed decision model is fair only when SF and PF are satisfied simultaneously, as interventions in individuals' sensitive attributes or those of their peers both yield equivalent outcomes. To achieve IAF, we develop a deep doubly robust framework to estimate and regularize SF and PF metrics for decision models. Extensive experiments on synthetic and real-world datasets validate our proposed concepts and methods.

Driven by empirical risk minimization, machine learning algorithm tends to exploit subtle statistical correlations existing in the training environment for prediction, while the spurious correlations are unstable across environments, leading to poor generalization performance. Accordingly, the problem of the Out-of-distribution (OOD) generalization aims to exploit an invariant/stable relationship between features and outcomes that generalizes well on all possible environments. To address the spurious correlation induced by the selection bias, in this article, we propose a novel Clique-based Causal Feature Separation (CCFS) algorithm by explicitly incorporating the causal structure to identify causal features of outcome for OOD generalization. Specifically, the proposed CCFS algorithm identifies the largest clique in the learned causal skeleton. Theoretically, we guarantee that either the largest clique or the rest of the causal skeleton is exactly the set of all causal features of the outcome. Finally, we separate the causal features from the non-causal ones with a sample-reweighting decorrelator for OOD prediction. Extensive experiments validate the effectiveness of the proposed CCFS method on both causal feature identification and OOD generalization tasks.

In causal inference, it is common to select a subset of observed covariates, named the adjustment features, to be adjusted for estimating the treatment effect. For real-world applications, the abundant covariates are usually observed, which contain extra variables partially correlating to the treatment (treatment-only variables, e.g., instrumental variables) or the outcome (outcome-only variables, e.g., precision variables) besides the confounders (variables that affect both the treatment and outcome). In principle, unbiased treatment effect estimation is achieved once the adjustment features contain all the confounders. However, the performance of empirical estimations varies a lot with different extra variables. To solve this issue, variable separation/selection for treatment effect estimation has received growing attention when the extra variables contain instrumental variables and precision variables.

In this paper, we focus on the problem of stable prediction across unknown test data, where the test distribution might be different from the training one and is always agnostic when model training. In such a case, previous machine learning methods might exploit subtly spurious correlations induced by non-causal variables in training data for prediction. Those spurious correlations are changeable across data, leading to instability of prediction across unknown test data. To address this problem, we propose a conditional independence test based algorithm to screen out part of non-causal features and reduce those spurious correlations for a more stable prediction by leveraging a seed variable. We show, both theoretically and with empirical experiments, that our algorithm can precisely screen out the isolated non-causal variables, which have no causal relationship with other variables, and remove the spurious correlations induced by them, increasing the stability of prediction across unknown test data. Extensive experiments on both synthetic and real-world datasets demonstrate that our algorithm outperforms state-of-the-art methods for stable prediction across unknown test data.

Learning individualized causal effect (ICE) plays a vital role in various fields of big data analysis, ranging from fine-grained policy evaluation to personalized treatment development. However, the presence of unmeasured confounders increases the difficulty of estimating ICE in real-world scenarios. A wide range of methods have been proposed to address the unmeasured confounders with the aid of instrument variable (IV), which sources from the treatment randomization. The performance of these methods relies on the well-predefined IVs that satisfy the unconfounded instruments assumption (i.e., the IVs are independent with the unmeasured confounders given observed covariates), which is untestable and leads to finding a valid IV becomes an art rather than science. In this paper, we focus on estimating the ICE with confounded instruments that violate the unconfounded instruments assumption. By considering the conditional independence between the set of confounded instruments and the outcome variable, we propose a novel method, named CVAE-IV, to generate a substitute of the unmeasured confounder with a conditional variational autoencoder. Our theoretical analysis guarantees that the generated confounder substitute will identify unbiased ICE. Extensive experiments on bias demand prediction and Mendelian randomization analysis verify the effectiveness of our method.

Beyond classical domain-specific adversarial training, a recently proposed task-specific framework has achieved a great success in single source domain adaptation by utilizing task-specific decision boundaries. However, compared to single-source-single-target setting, multi-source domain adaptation (MDA) shows more powerful capability to handle with most real-life cases. To align target domain with diverse multi-source domains using task-specific decision boundaries, we provide a deep insight of task-specific framework on MDA for the first time. Accordingly, we propose a novel task-specific multi-source domain adaptation method (TMDA) with a clustering embedded adversarial training process. Specifically, the proposed TMDA detects and refines less discriminative target representations through a max-min optimization over two adversarial task-specific classifiers. Moreover, our analysis implies that scattered multi-source representations disturb the adversarial training under the task-specific framework. To tight up the dispersed source representations, we embeds a relationship-based domain clustering into TMDA. Empirical results demonstrate that our TMDA outperforms state-of-the-art methods on toy dataset, sentiment analysis and digit classification.