To estimate the causal effect of an intervention, researchers need to identify a control group that represents what might have happened to the treatment group in the absence of that intervention. This is challenging without a randomized experiment and further complicated when few units (possibly only one) are treated. Nevertheless, when data are available on units over time, synthetic control (SC) methods provide an opportunity to construct a valid comparison by differentially weighting control units that did not receive the treatment so that their resulting pre-treatment trajectory is similar to that of the treated unit. The hope is that this weighted ``pseudo-counterfactual" can serve as a valid counterfactual in the post-treatment time period. Since its origin twenty years ago, SC has been used over 5,000 times in the literature (Web of Science, December 2025), leading to a proliferation of descriptions of the method and guidance on proper usage that is not always accurate and does not always align with what the original developers appear to have intended. As such, a number of accepted pieces of wisdom have arisen: (1) SC is robust to various implementations; (2) covariates are unnecessary, and (3) pre-treatment prediction error should guide model selection. We describe each in detail and conduct simulations that suggest, both for standard and alternative implementations of SC, that these purported truths are not supported by empirical evidence and thus actually represent misconceptions about best practice. Instead of relying on these misconceptions, we offer practical advice for more cautious implementation and interpretation of results.

翻译：为了估算干预措施的因果效应，研究者需要确定一个对照组，以代表在没有干预的情况下处理组本可能发生的情况。在没有随机实验的情况下，这一任务极具挑战性，当只有少数单位（可能仅一个）接受处理时则更为复杂。然而，当可获得随时间变化的单位数据时，合成控制（SC）方法通过赋予未接受处理的对照单位不同权重，使其处理前轨迹与处理单位相似，从而构建有效的比较，提供了一种可行途径。期望这种加权的“伪反事实”能在处理后时间段内作为有效的反事实。自二十年前提出以来，SC方法已在文献中被引用超过5,000次（Web of Science，2025年12月），导致该方法描述和使用指南的激增，但这些描述和指南并不总是准确，也不完全符合原始开发者最初的设计意图。因此，出现了一些公认的“智慧”： (1) SC对多种实现方式稳健； (2) 协变量并非必要； (3) 处理前预测误差应指导模型选择。我们详细阐述了每一点，并通过模拟研究表明，对于SC标准实现和替代实现，这些所谓的“真理”均缺乏实证支持，实际上是对最佳实践的误解。我们建议不要依赖这些误区，而是提供更谨慎的实施和结果解读的实用建议。