提出新算法同时评估行动时机与内容的因果效应。
Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do
- 基于递归Q函数设计，兼容Transformer等灵活序列模型。
- 在生存时间与肿瘤生长任务中实现精准因果效应估计。
- 适合需决策何时何事的医疗、金融、机器人场景。
医疗、机器人和金融等领域的问题需要同时推理行动内容与执行时机的价值。当前人工智能虽有望通过估计策略（如患者治疗方案或资源分配）的因果效应来辅助决策，但现有方法在处理不规则时间方面存在不足：要么对时间进行离散化，要么忽略时机策略的影响。本文提出一种新的深度Q算法——最早分歧Q评估（EDQ），可同时估计“何时”与“何事”的因果效应。EDQ采用与灵活序列模型（如Transformer）兼容的递归Q函数结构，在标准假设下提供准确估计。通过生存时间与肿瘤生长任务的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Problems in fields such as healthcare, robotics, and finance requires reasoning about the value both of what decision or action to take and when to take it. The prevailing hope is that artificial intelligence will support such decisions by estimating the causal effect of policies such as how to treat patients or how to allocate resources over time. However, existing methods for estimating the effect of a policy struggle with \emph{irregular time}. They either discretize time, or disregard the effect of timing policies. We present a new deep-Q algorithm that estimates the effect of both when and what to do called Earliest Disagreement Q-Evaluation (EDQ). EDQ makes use of recursion for the Q-function that is compatible with flexible sequence models, such as transformers. EDQ provides accurate estimates under standard assumptions. We validate the approach through experiments on survival time and tumor growth tasks.
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