Estimating treatment effects can assist decision-making in various areas, such as commerce and medicine. One application of the treatment effect estimation is to predict the effect of an advertisement on the purchase result of a customer, known as individual treatment effect (ITE). In online websites, the outcome of an individual can be affected by treatments of other individuals, as people often propagate information with their friends. This is referred to as interference. Prior studies have attempted to model interference for accurate ITE estimation under a static network among individuals. However, the network usually changes over time in real-world applications due to complex social activities among individuals. In this case, the outcomes of individuals can be interfered with not only by treatments for current neighbors but also by past information and treatments for past neighbors, which we refer to as dynamic interference . In this work, we model dynamic interference by developing an architecture to aggregate both the past information of individuals and their neighbors. Specifically, our proposed method contains an attention-based historical aggregation, which models interference received by individuals from previous timestamps, and an attention-based neighbor aggregation, which captures interference received by individuals within every timestamp. Since information about individuals changes over time, we propose a parameter evolution trick to adaptively update the parameters of the model, which enables the model to capture the dynamics effectively. In our experiments on multiple datasets with dynamic interference, our method outperforms existing methods for ITE estimation because they cannot capture dynamic interference, which corroborates the importance of dynamic interference modeling.

Abstract Causal inference plays a critical role in decision-making processes about whether to provide treatment to individuals across various domains, such as education, medicine, and e-commerce. One of the fundamental tasks in causal inference is to estimate the individual treatment effect (ITE), which represents the effect of a treatment on an individual outcome. Recently, many studies have focused on estimating ITE from graph data taking into account not only the covariates of units but also connections among them. In such a case, the outcome of a unit can be affected by not only its own covariates and treatment but also those of its neighbors, which is referred to as interference . Existing methods have utilized graph neural networks (GNNs) to capture interference and achieved improvements in estimating ITE on graph data. However, these methods are not computationally efficient and therefore cannot be applied to large graph data. To overcome this problem, we propose a novel method that reduces redundant computation in interference modeling while maintaining the prediction performance of ITE estimation. Our key idea is to model the propagation of interference by aggregating the information of neighbors before training and preserve the aggregated results for training our networks. We conduct intensive experiments on graph data consisting of up to a hundred thousand units and millions of edges. We show that the proposed method achieves superior or comparable performance to the existing GNN-based methods in ITE estimation, while the proposed method can be executed much faster than GNN-based methods.

Deep learning models have performed well on many NLP tasks. However, their internal mechanisms are typically difficult for humans to understand. The development of methods to explain models has become a key issue in the reliability of deep learning models in many important applications. Various saliency explanation methods, which give each feature of input a score proportional to the contribution of output, have been proposed to determine the part of the input which a model values most. Despite a considerable body of work on the evaluation of saliency methods, whether the results of various evaluation metrics agree with human cognition remains an open question. In this study, we propose a new human-based method to evaluate saliency methods in NLP by crowdsourcing. We recruited 800 crowd workers and empirically evaluated seven saliency methods on two datasets with the proposed method. We analyzed the performance of saliency methods, compared our results with existing automated evaluation methods, and identified notable differences between NLP and computer vision (CV) fields when using saliency methods. The instance-level data of our crowdsourced experiments and the code to reproduce the explanations are available at https://github.com/xtlu/lreccoling_evaluation.