Real-world observational datasets and machine learning have revolutionized data-driven decision-making, yet many models rely on empirical associations that may be misleading due to confounding and subgroup heterogeneity. Simpson’s paradox exemplifies this challenge, where aggregated and subgroup-level associations contradict each other, leading to misleading conclusions. Existing methods provide limited support for detecting and interpreting such paradoxical associations, especially for practitioners without deep causal expertise. We introduce De-paradox Tree , an interpretable algorithm designed to uncover hidden subgroup patterns behind paradoxical associations under assumed causal structures involving confounders and effect heterogeneity. It employs novel split criteria and balancing-based procedures to adjust for confounders and homogenize heterogeneous effects through recursive partitioning. Compared to state-of-the-art methods, De-paradox Tree builds simpler, more interpretable trees, selects relevant covariates, and identifies nested opposite effects while ensuring robust estimation of causal effects when causally admissible variables are provided. Our approach addresses the limitations of traditional causal inference and machine learning methods by introducing an interpretable framework that supports non-expert practitioners while explicitly acknowledging causal assumptions and scope limitations, enabling more reliable and informed decision-making in complex observational data environments.

Big data and machine learning tools have jointly empowered humans in making data-driven decisions. However, many of them capture empirical associations that might be spurious due to confounding factors and subgroup heterogeneity. The famous Simpson's paradox is such a phenomenon where aggregated and subgroup-level associations contradict with each other, causing cognitive confusions and difficulty in making adequate interpretations and decisions. Existing tools provide little insights for humans to locate, reason about, and prevent pitfalls of spurious association in practice. We propose VISPUR, a visual analytic system that provides a causal analysis framework and a human-centric workflow for tackling spurious associations. These include a CONFOUNDER DASHBOARD, which can automatically identify possible confounding factors, and a SUBGROUP VIEWER, which allows for the visualization and comparison of diverse subgroup patterns that likely or potentially result in a misinterpretation of causality. Additionally, weproposea REASONING STORYBOARD, which uses a flow-based approach to illustrate paradoxical phenomena, as well as an interactive DECISION DIAGNOSIS panel that helps ensure accountable decision-making. Through an expert interview and a controlled user experiment, our qualitative and quantitative results demonstrate that the proposed "de-paradox" workflow and the designed visual analytic system are effective in helping human users to identify and understand spurious associations, as well as to make accountable causal decisions.

Social media has become an important source where people gather and communicate news. Prior studies in conventional mass media suggest that gatekeepers play an important role in the production of news messages. Despite the initial claim of social media being a place of democratized participation, we now know, social media is not free of gatekeepers either. However, it is unclear who social media gatekeepers are, how to identify them, and most importantly how do they impact news content production and dissemination. Due to fundamental differences between the structure and workings of social media vs. traditional media, what we know from mass media cannot directly apply in the context of social media. To answer these questions, we propose an actionable definition of social media gatekeepers backed by literature on news reporting in social media and traditional mass media. We then present a case study of identifying gatekeepers on Twitter at scale, using a set of 70k Twitter users interested in the news topic of "immigration''. The results of our mixed research approach highlight that, unlike the general Twitter users, the Twitter gatekeepers are often self-determining citizen journalists who manage their media presentation strategically. Moreover, Twitter gatekeepers tend to exhibit behavior mostly in accordance with the journalism norms and they contribute to and guard the truthfulness and neutrality of content.

Forecasting patients' disease progressions with rich longitudinal clinical data has drawn much attention in recent years due to its impactful application in healthcare and the medical field. Researchers have tackled this problem by leveraging traditional machine learning, statistical techniques and deep learning based models. However, existing methods suffer from either deterministic internal structures or over-simplified stochastic components, failing to deal with complex uncertain scenarios such as progression uncertainty (i.e., multiple possible trajectories) and data uncertainty (i.e., imprecise observations and misdiagnosis). To overcome these major uncertainty issues, we propose a novel deep generative model, Stochastic Disease Forecasting Model (StoCast), along with an associated neural network architecture StoCastNet, that can be trained efficiently via stochastic optimization techniques. Our StoCast model uses internal stochastic components to deal with departures of observed data from patients' true health states, and more importantly, is able to produce a comprehensive estimate of future disease progression trajectories. Based on two public datasets related to Alzheimer's disease and Parkinson's disease, we demonstrate how our StoCast model achieves robust and superior performance than deterministic baseline approaches, and conveys richer information that can potentially assist doctors to make decisions with greater confidence in a complex uncertain scenario.

The increasing and flexible use of autonomous systems in many domains -- from intelligent transportation systems, information systems, to business transaction management -- has led to challenges in understanding the "normal" and "abnormal" behaviors of those systems. As the systems may be composed of internal states and relationships among sub-systems, it requires not only warning users to anomalous situations but also provides "transparency" about how the anomalies deviate from normalcy for more appropriate intervention. We propose a unified anomaly discovery framework "DeepSphere" that simultaneously meet the above two requirements -- identifying the anomalous cases and further exploring the cases' anomalous structure localized in spatial and temporal context. DeepSphere leverages deep autoencoders and hypersphere learning methods, having the capability of isolating anomaly pollution and reconstructing normal behaviors. DeepSphere does not rely on human annotated samples and can generalize to unseen data. Extensive experiments on both synthetic and real datasets demonstrate the consistent and robust performance of the proposed method.

The increasing availability of spatiotemporal data continuously collected from various sources provides new opportunities for a timely understanding of the data in their spatial and temporal context. Finding abnormal patterns in such data poses significant challenges. Given that there is often no clear boundary between normal and abnormal patterns, existing solutions are limited in their capacity of identifying anomalies in large, dynamic and heterogeneous data, interpreting anomalies in their multifaceted, spatiotemporal context, and allowing users to provide feedback in the analysis loop. In this work, we introduce a unified visual interactive system and framework, Voila, for interactively detecting anomalies in spatiotemporal data collected from a streaming data source. The system is designed to meet two requirements in real-world applications, i.e., online monitoring and interactivity. We propose a novel tensor-based anomaly analysis algorithm with visualization and interaction design that dynamically produces contextualized, interpretable data summaries and allows for interactively ranking anomalous patterns based on user input. Using the "smart city" as an example scenario, we demonstrate the effectiveness of the proposed framework through quantitative evaluation and qualitative case studies.

Detecting anomalous patterns from dynamic and multi-attributed network systems has been a challenging problem due to the complication of temporal dynamics and the variations reflected in multiple data sources. We propose a Multi-view Time-Series Hypersphere Learning (MTHL) approach that leverages multi-view learning and support vector description to tackle this problem. Given a dynamic network with time-varying edge and node properties, MTHL projects multi-view time-series data into a shared latent subspace, and then learns a compact hypersphere surrounding normal samples with soft constraints. The learned hypersphere allows for effectively distinguishing normal and abnormal cases. We further propose an efficient, two-stage alternating optimization algorithm as a solution to the MTHL. Extensive experiments are conducted on both synthetic and real datasets. Results demonstrate that our method outperforms the state-of-the-art baseline methods in detecting three types of events that involve (i) time-varying features alone, (ii) time-aggregated features alone, as well as (iii) both features. Moreover, our approach exhibits consistent and good performance in face of issues including noises, anomaly pollution in training phase and data imbalance.