论文标题
动态潜在状态模型中的反事实分析
Counterfactual Analysis in Dynamic Latent State Models
论文作者
论文摘要
我们提供了一个基于优化的框架，以在具有隐藏状态的动态模型中执行反事实分析。我们的框架基于``绑架，行动和预测''的方法来回答反事实查询并处理（1）状态隐藏的两个关键挑战，并且（2）模型是动态的。认识到缺乏对基本因果机制的知识以及无限多种此类机制的可能性，我们在该空间上进行了优化，并计算了相反的兴趣数量的上限和下限。我们的工作汇集了因果关系，州空间模型，模拟和优化的想法，我们将其应用于乳腺癌案例研究。据我们所知，我们是第一个在动态潜在模型中计算反事实查询上的下层和上限的人。
We provide an optimization-based framework to perform counterfactual analysis in a dynamic model with hidden states. Our framework is grounded in the ``abduction, action, and prediction'' approach to answer counterfactual queries and handles two key challenges where (1) the states are hidden and (2) the model is dynamic. Recognizing the lack of knowledge on the underlying causal mechanism and the possibility of infinitely many such mechanisms, we optimize over this space and compute upper and lower bounds on the counterfactual quantity of interest. Our work brings together ideas from causality, state-space models, simulation, and optimization, and we apply it on a breast cancer case study. To the best of our knowledge, we are the first to compute lower and upper bounds on a counterfactual query in a dynamic latent-state model.
