Toward a practical handbook for choosing among causal inference methods in non-randomized studies with binary outcomes: A simulation study for applied researchers
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非随机研究中二元结局因果推断方法的选择手册：模拟研究
- 研究问题
- 在二元处理与二元结局的观察性研究中，样本规模、处理及结局比例、效应大小与假设违反如何影响因果方法的选择？
- 方法与思路
- 作者通过大规模模拟比较倾向得分完全匹配、逆概率加权、G 计算和目标最大似然估计，并在两个临床数据集应用选择手册。
- 材料陈述的贡献
- 摘要明确提出面向应用研究者的方法选择手册，并以轻症 COVID-19 与结直肠手术数据考察其实用性，未报告具体优胜结果。
- 阅读关注点
- 应核对模拟的数据生成机制、目标估计量、假设违反方式与比较指标，并检查真实数据中的选择规则是否与模拟条件一致。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未给出方法选择规则或比较结果，不能判断哪种方法普遍更好。
原文摘要
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).