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.

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.

Massive amounts of data are the foundation of data-driven recommendation models. As an inherent nature of big data, data heterogeneity widely exists in real-world recommendation systems. It reflects the differences in the properties among sub-populations. Ignoring the heterogeneity in recommendation data could mislead the models, hurt the sub-populational robustness, and finally limit the performance of recommendation models. However, data heterogeneity has not received substantial attention within the recommendation community, prompting us to adequately explore and exploit data heterogeneity to solve these challenges and enhance data analysis. In this study, we specifically focus on two representative categories of heterogeneity in recommendation data: heterogeneity of prediction mechanism and covariate distribution. To explore the data heterogeneity, we propose an algorithm based on bilevel clustering. Additionally, we demonstrate how the explored data heterogeneity can be exploited for prediction and debias in recommendation scenarios, specifically by building models using multiple sub-models and augmenting the propensity score estimation. Extensive experiments conducted on real-world data substantiate the existence of heterogeneity in recommendation data and validate the effectiveness of exploring and exploiting data heterogeneity in improving recommendation performance.

Recommendation systems are frequently challenged by pervasive biases in the training set that can compromise model effectiveness. To address this issue, various debiasing techniques have been developed to eliminate biases and produce debiased models. However, when encountering varying test environments, some data patterns manifested by the training data could be beneficial to the model’s performance. Completely removing biases may overlook the beneficial data patterns and consequently diminish recommendation accuracy. Thus, it is crucial to carefully integrate certain biases to optimize performance, while the ideal level of bias integration is highly dependent on the test environment. Moreover, these systems operate in dynamic scenarios where the test environments could vary, necessitating an adaptive integration strategy customized to the environment. Our research establishes that discrepancies in predictions of models can guide the selection of the most fitting model for specific situations. Building on this understanding, we present AdaptSel, a pioneering method for the adaptive selection of the superior model during the testing phase. Empirical evaluations substantiate the foundational assumptions of AdaptSel, accentuating its effectiveness in adaptively selecting the most suitable model for varying test environments.

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.

Offline policy evaluation (OPE) aims to accurately estimate the performance of a hypothetical policy using only historical data, which has drawn increasing attention in a wide range of applications including recommender systems and personalized medicine. With the presence of rising granularity of consumer data, many industries started exploring larger action candidate spaces to support more precise personalized action. While inverse propensity score (IPS) is a standard OPE estimator, it suffers from more severe variance issues with increasing action spaces. To address this issue, we theoretically prove that the estimation variance can be reduced by merging actions into groups while the distinction among these action effects on the outcome can induce extra bias. Motivated by these, we propose a novel IPS estimator with outcome-oriented action Grouping (GroupIPS), which leverages a Lipschitz regularized network to measure the distance of action effects in the embedding space and merges nearest action neighbors. This strategy enables more robust estimation by achieving smaller variances while inducing minor additional bias. Empirically, extensive experiments on both synthetic and real world datasets demonstrate the effectiveness of our proposed method.

In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios, leading to the sharp decline of performance in testing environments. It is even more severe in many common applications where only the implicit feedback from sparse data is available. Hence, it is crucial to promote the performance stability of recommendation method in different environments. In this work, we first make a thorough analysis of implicit recommendation problem from the viewpoint of out-of-distribution (OOD) generalization. Then under the guidance of our theoretical analysis, we propose to incorporate the recommendation-specific DAG learner into a novel causal preference-based recommendation framework named CausPref, mainly consisting of causal learning of invariant user preference and anti-preference negative sampling to deal with implicit feedback. Extensive experimental results from real-world datasets clearly demonstrate that our approach surpasses the benchmark models significantly under types of out-of-distribution settings, and show its impressive interpretability.

Causal Inference plays an important role in decision making in many fields, such as social marketing, healthcare, and public policy. One fundamental problem in causal inference is the treatment effect estimation in observational studies when variables are confounded. Controlling for confounding effects is generally handled by propensity score. But it treats all observed variables as confounders and ignores the adjustment variables, which have no influence on treatment but are predictive of the outcome. Recently, it has been demonstrated that the adjustment variables are effective in reducing the variance of the estimated treatment effect. However, how to automatically separate the confounders and adjustment variables in observational studies is still an open problem, especially in the scenarios of high dimensional variables, which are common in the big data era. In this paper, we first propose a Data-Driven Variable Decomposition (D$^2$VD) algorithm, which can 1) automatically separate confounders and adjustment variables with a data-driven approach, and 2) simultaneously estimate treatment effect in observational studies with high dimensional variables. Under standard assumptions, we theoretically prove that our D$^2$VD algorithm can unbiased estimate treatment effect and achieve lower variance than traditional propensity score based methods. Moreover, to address the challenges from high-dimensional variables and nonlinear, we extend our D$^2$VD to a non-linear version, namely Nonlinear-D$^2$VD (N-D$^2$VD) algorithm. To validate the effectiveness of our proposed algorithms, we conduct extensive experiments on both synthetic and real-world datasets. The experimental results demonstrate that our D$^2$VD and N-D$^2$VD algorithms can automatically separate the variables precisely, and estimate treatment effect more accurately and with tighter confidence intervals than the state-of-the-art methods. We also demonstrated that the top-ranked features by our algorithm have the best prediction performance on an online advertising dataset.

Nowadays graph has become a general and powerful representation to describe the rich relationships among different kinds of entities via the underlying patterns encoded in its structure. The knowledge (more generally) accumulated in graph is expected to be able to cross populations from one to another and the past to future. However the data collection process of graph generation is full of known or unknown sample selection biases, leading to spurious correlations among entities, especially in the non-stationary and heterogeneous environments. In this paper, we target the problem of learning stable graphs from multiple environments with selection bias. We purpose a Stable Graph Learning (SGL) framework to learn a graph that can capture general relational patterns which are irrelevant with the selection bias in an unsupervised way. Extensive experimental results from both simulation and real data demonstrate that our method could significantly benefit the generalization capacity of graph structure.

Precisely evaluating the effect of new policies (e.g. ad-placement models, recommendation functions, ranking functions) is one of the most important problems for improving interactive systems. The conventional policy evaluation methods rely on online A/B tests, but they are usually extremely expensive and may have undesirable impacts. Recently, Inverse Propensity Score (IPS) estimators are proposed as alternatives to evaluate the effect of new policy with offline logged data that was collected from a different policy in the past. They tend to remove the distribution shift induced by past policy. However, they ignore the distribution shift that would be induced by the new policy, which results in imprecise evaluation. Moreover, their performances rely on accurate estimation of propensity score, which can not be guaranteed or validated in practice. In this paper, we propose a non-parametric method, named Focused Context Balancing (FCB) algorithm, to learn sample weights for context balancing, so that the distribution shift induced by the past policy and new policy can be eliminated respectively. To validate the effectiveness of our FCB algorithm, we conduct extensive experiments on both synthetic and real world datasets. The experimental results clearly demonstrate that our FCB algorithm outperforms existing estimators by achieving more precise and robust results for offline policy evaluation.