论文标题
通过不确定性意识到神经微分方程来预测治疗的影响
Predicting the impact of treatments over time with uncertainty aware neural differential equations
论文作者
论文摘要
尽管最近的时间序列建模取得了重大进展，但从观察数据中预测治疗的影响仍然代表了一个大调。治疗分配通常与响应的预测指标相关，从而导致缺乏反事实预测的数据支持，从而进行质量较差。因果推断的发展已导致方法通过需要最低水平重叠来解决这种混杂问题。但是，重叠很难评估，通常在实践中不满意。在这项工作中，我们提出了反事实ODE（CF-ODE），这是一种使用配备有不确定性估计值的神经普通微分方程来预测治疗随时间不断预测的新方法。这允许明确评估可以可靠地预测哪些治疗结果。我们在几个纵向数据集中证明了CF-ODE比以前可用的方法提供了更准确的预测和更可靠的不确定性估计。
Predicting the impact of treatments from observational data only still represents a majorchallenge despite recent significant advances in time series modeling. Treatment assignments are usually correlated with the predictors of the response, resulting in a lack of data support for counterfactual predictions and therefore in poor quality estimates. Developments in causal inference have lead to methods addressing this confounding by requiring a minimum level of overlap. However,overlap is difficult to assess and usually notsatisfied in practice. In this work, we propose Counterfactual ODE (CF-ODE), a novel method to predict the impact of treatments continuously over time using Neural Ordinary Differential Equations equipped with uncertainty estimates. This allows to specifically assess which treatment outcomes can be reliably predicted. We demonstrate over several longitudinal data sets that CF-ODE provides more accurate predictions and more reliable uncertainty estimates than previously available methods.
