Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
<|reference_start|>Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems: Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time. For example, the COVID-19 transmission in the U.S. can be viewed as a multi-agent system, where states act as agents and daily population movements between them are interactions. Estimating the counterfactual outcomes in such systems enables accurate future predictions and effective decision-making, such as formulating COVID-19 policies. However, existing methods fail to model the continuous dynamic effects of treatments on the outcome, especially when multiple treatments (e.g., "stay-at-home" and "get-vaccine" policies) are applied simultaneously. To tackle this challenge, we propose Causal Graph Ordinary Differential Equations (CAG-ODE), a novel model that captures the continuous interaction among agents using a Graph Neural Network (GNN) as the ODE function. The key innovation of our model is to learn time-dependent representations of treatments and incorporate them into the ODE function, enabling precise predictions of potential outcomes. To mitigate confounding bias, we further propose two domain adversarial learning-based objectives, which enable our model to learn balanced continuous representations that are not affected by treatments or interference. Experiments on two datasets (i.e., COVID-19 and tumor growth) demonstrate the superior performance of our proposed model.<|reference_end|>
@article{huang2024causal,
    title={Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent
  Dynamical Systems},
    author={Zijie Huang, Jeehyun Hwang, Junkai Zhang, Jinwoo Baik, Weitong Zhang,
  Dominik Wodarz, Yizhou Sun, Quanquan Gu, Wei Wang},
    journal={arXiv preprint arXiv:2403.00178},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00178},
    primaryClass={cs.LG cs.AI}
}