Continual Causal Effect Estimation: Challenges and Opportunities

A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from only one source. This practical concern of accessibility is ubiquitous in various academic and industrial applications. That's what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data, i.e., the extensibility for incrementally available observational data, the adaptability for extra domain adaptation problem except for the imbalance between treatment and control groups, and the accessibility for an enormous amount of data. In this position paper, we formally define the problem of continual treatment effect estimation, describe its research challenges, and then present possible solutions to this problem. Moreover, we will discuss future research directions on this topic.Comment: The 37th AAAI conference on artificial intelligence Continual Causality Bridge Progra

Fair Attribute Completion on Graph with Missing Attributes

Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair graph learning simply assumes that attributes of all nodes are available for model training and then makes fair predictions. In practice, however, the attributes of some nodes might not be accessible due to missing data or privacy concerns, which makes fair graph learning even more challenging. In this paper, we propose FairAC, a fair attribute completion method, to complement missing information and learn fair node embeddings for graphs with missing attributes. FairAC adopts an attention mechanism to deal with the attribute missing problem and meanwhile, it mitigates two types of unfairness, i.e., feature unfairness from attributes and topological unfairness due to attribute completion. FairAC can work on various types of homogeneous graphs and generate fair embeddings for them and thus can be applied to most downstream tasks to improve their fairness performance. To our best knowledge, FairAC is the first method that jointly addresses the graph attribution completion and graph unfairness problems. Experimental results on benchmark datasets show that our method achieves better fairness performance with less sacrifice in accuracy, compared with the state-of-the-art methods of fair graph learning. Code is available at: https://github.com/donglgcn/FairAC

How People Perceive The Dynamic Zero-COVID Policy: A Retrospective Analysis From The Perspective of Appraisal Theory

The Dynamic Zero-COVID Policy in China spanned three years and diverse emotional responses have been observed at different times. In this paper, we retrospectively analyzed public sentiments and perceptions of the policy, especially regarding how they evolved over time, and how they related to people's lived experiences. Through sentiment analysis of 2,358 collected Weibo posts, we identified four representative points, i.e., policy initialization, sharp sentiment change, lowest sentiment score, and policy termination, for an in-depth discourse analysis through the lens of appraisal theory. In the end, we reflected on the evolving public sentiments toward the Dynamic Zero-COVID Policy and proposed implications for effective epidemic prevention and control measures for future crises

Revealing two radio active galactic nuclei extremely near PSR J0437−-4715

Newton's gravitational constant $G$ may vary with time at an extremely low level. The time variability of $G$ will affect the orbital motion of a millisecond pulsar in a binary system and cause a tiny difference between the orbital period-dependent measurement of the kinematic distance and the direct measurement of the annual parallax distance. PSR J0437$-$4715 is the nearest millisecond pulsar and the brightest at radio. To explore the feasibility of achieving a parallax distance accuracy of one light-year, comparable to the recent timing result, with the technique of differential astrometry, we searched for compact radio sources quite close to PSR J0437$-$4715. Using existing data from the Very Large Array and the Australia Telescope Compact Array, we detected two sources with flat spectra, relatively stable flux densities of 0.9 and 1.0 mJy at 8.4 GHz and separations of 13 and 45 arcsec. With a network consisting of the Long Baseline Array and the Kunming 40-m radio telescope, we found that both sources have a point-like structure and a brightness temperature of $\geq$10$^7$ K. According to these radio inputs and the absence of counterparts in the other bands, we argue that they are most likely the compact radio cores of extragalactic active galactic nuclei rather than Galactic radio stars. The finding of these two radio active galactic nuclei will enable us to achieve a sub-pc distance accuracy with the in-beam phase-referencing very-long-baseline interferometric observations and provide one of the most stringent constraints on the time variability of $G$ in the near future.Comment: 9 pages, 3 tables, 3 figures. Accepted for publication in MNRA