Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare by assisting decision-makers to answer ''what-iF'' questions. Existing causal inference approaches typically consider regular, discrete-time intervals between observations and treatment decisions and hence are unable to naturally model irregularly sampled data, which is the common setting in practice. To handle arbitrary observation patterns, we interpret the data as samples from an underlying continuous-time process and propose to model its latent trajectory explicitly using the mathematics of controlled differential equations. This leads to a new approach, the Treatment Effect Neural Controlled Differential Equation (TE-CDE), that allows the potential outcomes to be evaluated at any time point. In addition, adversarial training is used to adjust for time-dependent confounding which is critical in longitudinal settings and is an added challenge not encountered in conventional time-series. To assess solutions to this problem, we propose a controllable simulation environment based on a model of tumor growth for a range of scenarios with irregular sampling reflective of a variety of clinical scenarios. TE-CDE consistently outperforms existing approaches in all simulated scenarios with irregular sampling.
翻译:通过帮助决策者回答“What-iF”问题,估计反事实的长期结果有可能释放个人化保健。现有的因果推断方法通常考虑到观察和治疗决定之间的定期、离散的时间间隔,因此无法自然地模拟非常规抽样数据,而这是实践中常见的情况。为了处理任意的观察模式,我们将数据解释为来自一个潜在的连续时间过程的样本,并提议明确使用受控差异方程的数学来模拟其潜在轨迹。这导致一种新的方法,即治疗效应神经控制差异方程(TE-CDE),允许在任何时间点对潜在结果进行评估。此外,还利用对抗性训练来适应在长视环境中至关重要且在常规时间序列中未遇到的附加挑战,为了评估这一问题的解决办法,我们提议一种可控制的模拟环境,以肿瘤增长模型为基础,对各种临床情景进行不规则的抽样反射。Te-CDE在所有模拟假设情景中一贯地超越现有方法,采用不定期取样。