For approximately half of the individuals receiving mental health care, the results are suboptimal, even when treatments align with evidence-based guidelines. These limited effects may partly stem from how clinical decisions on treatment focus are made in mental health care. Typically, treatment strategy is guided by the diagnostic classification combined with the individualized case conceptualization. While standard, this approach may fall short for several reasons such as biases on the part of both the patient and therapist, and treatment guidelines being based on average effects that may not (exactly) suit the individual patient. To address these challenges, we propose a novel framework that reduces biases in clinical decision-making and makes it genuinely possible to tailor treatment focus to the individual patient. This framework involves (a) constructing causal graphs and estimating causal effects from intensively collected, longitudinal patient data, (b) simulating new time series based upon the causal relationships, and (c) using these simulations to identify the most effective treatment focus for the individual patient. By simulating and comparing different intervention strategies and examining both the estimated individual's responsiveness and its long-term effectiveness, this approach may generate useful insights to guide treatment focus and strategy, which can lead to a significant improvement of treatment outcomes in mental health care.

翻译：约半数接受心理治疗的患者面临的疗效欠佳，即便治疗方式遵循循证指南。这种局限性可能部分源于精神健康领域制定治疗重点的临床决策方式。当前的治疗策略通常由诊断分类结合个案概念化共同指导。尽管这是标准做法，但该方法可能因患者及治疗师的认知偏差、基于群体平均效应制定的治疗指南未必（完全）适用于个体患者等原因而存在不足。为解决这些挑战，我们提出一种新型框架，可减少临床决策偏差，并真正实现治疗重点的个体化定制。该框架包含：（a）基于密集采集的纵向患者数据构建因果图并估算因果效应；（b）根据因果关系模拟生成新的时间序列；（c）利用这些模拟确定对个体患者最有效的治疗重点。通过模拟比较不同干预策略，同时评估个体预估响应度及其长期疗效，该方法可为指导治疗重点与策略生成有价值的洞见，从而显著提升精神健康领域的治疗成效。