Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917601215512576 |
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| author | Huang, Zijie Hwang, Jeehyun Zhang, Junkai Baik, Jinwoo Zhang, Weitong Wodarz, Dominik Sun, Yizhou Gu, Quanquan Wang, Wei |
| author_facet | Huang, Zijie Hwang, Jeehyun Zhang, Junkai Baik, Jinwoo Zhang, Weitong Wodarz, Dominik Sun, Yizhou Gu, Quanquan Wang, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00178 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems Huang, Zijie Hwang, Jeehyun Zhang, Junkai Baik, Jinwoo Zhang, Weitong Wodarz, Dominik Sun, Yizhou Gu, Quanquan Wang, Wei Machine Learning Artificial Intelligence 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. |
| title | Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.00178 |