CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems

• URL: http://arxiv.org/abs/2306.11216v1
• Date: Tue, 20 Jun 2023 00:50:09 GMT
• Title: CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems
• Authors: Song Jiang, Zijie Huang, Xiao Luo, Yizhou Sun
• Abstract summary: We study how to estimate counterfactual outcomes in multi-agent dynamical systems. Existing studies of causal inference over time rely on the assumption that units are mutually independent. We propose CounterFactual GraphODE, a causal model that estimates continuous-time counterfactual outcomes.
• Score: 29.358010668392208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent dynamical systems refer to scenarios where multiple units interact with each other and evolve collectively over time. To make informed decisions in multi-agent dynamical systems, such as determining the optimal vaccine distribution plan, it is essential for decision-makers to estimate the continuous-time counterfactual outcomes. However, existing studies of causal inference over time rely on the assumption that units are mutually independent, which is not valid for multi-agent dynamical systems. In this paper, we aim to bridge this gap and study how to estimate counterfactual outcomes in multi-agent dynamical systems. Causal inference in a multi-agent dynamical system has unique challenges: 1) Confounders are time-varying and are present in both individual unit covariates and those of other units; 2) Units are affected by not only their own but also others' treatments; 3) The treatments are naturally dynamic, such as receiving vaccines and boosters in a seasonal manner. We model a multi-agent dynamical system as a graph and propose CounterFactual GraphODE (CF-GODE), a causal model that estimates continuous-time counterfactual outcomes in the presence of inter-dependencies between units. To facilitate continuous-time estimation, we propose Treatment-Induced GraphODE, a novel ordinary differential equation based on GNN, which incorporates dynamical treatments as additional inputs to predict potential outcomes over time. To remove confounding bias, we propose two domain adversarial learning based objectives that learn balanced continuous representation trajectories, which are not predictive of treatments and interference. We further provide theoretical justification to prove their effectiveness. Experiments on two semi-synthetic datasets confirm that CF-GODE outperforms baselines on counterfactual estimation. We also provide extensive analyses to understand how our model works.

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