Sequential positivity is often a necessary assumption for drawing causal inferences, such as through marginal structural modeling. Unfortunately, verification of this assumption can be challenging because it usually relies on multiple parametric propensity score models, unlikely all correctly specified. Therefore, we propose a new algorithm, called "sequential Positivity Regression Tree" (sPoRT), to check this assumption with greater ease under either static or dynamic treatment strategies. This algorithm also identifies the subgroups found to be violating this assumption, allowing for insights about the nature of the violations and potential solutions. We first present different versions of sPoRT based on either stratifying or pooling over time. Finally, we illustrate its use in a real-life application of HIV-positive children in Southern Africa with and without pooling over time. An R notebook showing how to use sPoRT is available at github.com/ArthurChatton/sPoRT-notebook.

翻译：序贯正性通常是进行因果推断（例如通过边际结构模型）的必要假设。然而，验证这一假设可能具有挑战性，因为它通常依赖于多个参数化的倾向得分模型，而这些模型不太可能全部被正确设定。为此，我们提出了一种名为“序贯正性回归树”（sPoRT）的新算法，以便在静态或动态治疗策略下更便捷地检验该假设。该算法还能识别出违反该假设的亚组，从而有助于理解违反正的本质并探索潜在的解决方案。我们首先提出了基于时间分层或时间合并的不同版本sPoRT。最后，我们通过南非HIV阳性儿童的实际应用案例，分别展示了采用时间合并与不合并策略时sPoRT的使用方法。展示sPoRT使用方法的R笔记本可在github.com/ArthurChatton/sPoRT-notebook获取。