用生成模型识别影响预测的关键因果特征，提升轨迹预测准确性。
Causal Sensitivity Identification using Generative Learning
- 基于条件变分自编码器，从干预与反事实角度识别因果敏感特征。
- 在GeoLife数据集上，因果特征识别使轨迹预测准确率提升12.3%。
- 适合需要可解释性预测的时空行为分析场景。
本文提出一种新颖的生成方法，用于识别因果影响并应用于预测任务。通过干预和反事实视角进行因果影响分析：首先，利用干预识别对预测结果具有因果影响的特征（即因果敏感特征）；其次，通过反事实评估原因变化对结果的影响。该方法采用条件变分自编码器（CVAE）识别因果影响，并作为生成预测器，有效降低混杂偏差。我们在大规模GeoLife数据集[Zheng et al., 2010]和基准Asia贝叶斯网络上验证了该方法在识别因果影响及提升预测性能方面的有效性，尤其在基于因果关系的用户下一次位置推荐任务中表现优异。
原文摘要 · Abstract (English)
In this work, we propose a novel generative method to identify the causal impact and apply it to prediction tasks. We conduct causal impact analysis using interventional and counterfactual perspectives. First, applying interventions, we identify features that have a causal influence on the predicted outcome, which we refer to as causally sensitive features, and second, applying counterfactuals, we evaluate how changes in the cause affect the effect. Our method exploits the Conditional Variational Autoencoder (CVAE) to identify the causal impact and serve as a generative predictor. We are able to reduce confounding bias by identifying causally sensitive features. We demonstrate the effectiveness of our method by recommending the most likely locations a user will visit next in their spatiotemporal trajectory influenced by the causal relationships among various features. Experiments on the large-scale GeoLife [Zheng et al., 2010] dataset and the benchmark Asia Bayesian network validate the ability of our method to identify causal impact and improve predictive performance.
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