Causal Sensitivity Identification using Generative Learning

2025-09-01

AI Summary

This paper addresses the challenges of identifying causally sensitive features and mitigating severe confounding bias in spatiotemporal trajectory prediction. We propose a generative learning–based framework for causal sensitivity identification. Methodologically, it integrates intervention analysis and counterfactual reasoning within a conditional variational autoencoder (CVAE) architecture, jointly modeling feature-level causal effects and trajectory generation to enable end-to-end optimization of causal discovery and prediction. Our key contribution is the first application of generative modeling to causal sensitivity identification, explicitly disentangling confounders to enhance model interpretability and out-of-distribution robustness. Experiments on the GeoLife trajectory dataset and the Asia Bayesian network benchmark demonstrate that our approach significantly outperforms existing causal prediction models, achieving state-of-the-art performance in both causal identification accuracy and trajectory prediction precision.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Identifying causally sensitive features using generative learning methods
• Reducing confounding bias in predictive tasks through causal analysis
• Applying causal impact to spatiotemporal trajectory prediction tasks

Innovation

• Generative CVAE method identifies causal features
• Uses interventional and counterfactual causal analysis
• Reduces confounding bias in predictive tasks