Applied researchers in biomedicine and related fields are often interested in estimating the causal effect of a treatment or intervention. Although randomized clinical trials are considered the gold standard for establishing causal effects, they are not always feasible, and real-world data may represent the only available source of evidence. In such settings, causal effects must be estimated using statistical methods applied to observational data. Over the last few decades, modern causal inference methods based on the potential outcomes framework have emerged as useful tools in this field. However, many such techniques exist, and their performance depends on factors such as sample size, the proportion of treated patients, the proportion of patients experiencing the outcome, the magnitude of the treatment effect, the target estimand, and potential violations of the fundamental assumptions of causal inference. Given the wide range of available methods, selecting an appropriate approach can be challenging for applied researchers. This study uses a large-scale simulation experiment to address this issue and provide researchers with a guide in the form of a handbook for a binary treatment and a binary outcome. Particularly, we test four popular statistical techniques: propensity score matching (full matching), inverse of the probability weighting, G-computation, and targeted maximum likelihood estimation. The proposed handbook is applied to two real-world datasets to assess its practical utility: one comprising vulnerable patients with mild COVID-19 (n=534 patients and more than 50% treated), and another of patients undergoing colorectal surgery (n=3635 patients and about 20% treated).

翻译：生物医学及相关领域的应用研究者常关注治疗或干预措施的因果效应估计。尽管随机临床试验被视为建立因果效应的金标准，但其可行性常受限，真实世界数据可能成为唯一可用的证据来源。在此类情境下，需通过观察性数据应用统计方法估计因果效应。近几十年来，基于潜在结果框架的现代因果推断方法已成为该领域的重要工具。然而，现有方法众多，其性能取决于样本量、治疗组患者比例、结局发生患者比例、治疗效应幅度、目标估计量以及因果推断基本假设的潜在违背情况等因素。面对广泛可用的方法，应用研究者选择合适的分析策略颇具挑战。本研究通过大规模模拟实验解决该问题，以二元治疗与二元结局为场景，为研究者提供手册形式的指导。具体而言，我们测试了四种主流统计技术：倾向性评分匹配（完全匹配）、逆概率加权、G-计算法及目标最大似然估计。该手册在两个真实世界数据集中验证其实用性：其一为轻症COVID-19脆弱患者队列（n=534，治疗组占比>50%），其二为结直肠手术患者队列（n=3635，治疗组占比约20%）。