We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational Inequality (VI) formulation, we cast the causal discovery problem as a general convex optimization. Furthermore, we develop a non-asymptotic recovery guarantee and quantifiable uncertainty by solving a linear program to establish confidence intervals for a wide range of non-linear monotone link functions. We validate our theoretical results and show the competitive performance of our method via extensive numerical experiments. Most importantly, we demonstrate the effectiveness of our approach in recovering highly interpretable causal DAGs over Sepsis Associated Derangements (SADs) while achieving comparable prediction performance to powerful ``black-box'' models such as XGBoost. Thus, the future adoption of our proposed method to conduct continuous surveillance of high-risk patients by clinicians is much more likely.
翻译:我们提出了一个普遍的线性结构性因果模型,加上一个新的数据适应性线性调整模式,以便从时间序列中恢复因果性直线图(DAGs)。我们利用最近开发的随机单色单色变异不平等(VI)配方,将因果发现问题作为一般二次曲线优化。此外,我们开发了一个非无损恢复保障和量化不确定性,方法是解决一个线性方案,为一系列非线性单质链接功能建立信任间隔。我们验证了我们的理论结果,并通过广泛的数字实验展示了我们方法的竞争性性能。最重要的是,我们展示了我们的方法的有效性,在恢复高度可解释的单色谱相关变异(SADs)时,取得了与强大的“黑盒”模型(如XGBoost)可比的预测性性能。因此,我们今后更有可能采用我们所建议的方法,由临床医生对高风险病人进行持续监测。