Matching and weighting methods for observational studies involve the choice of an estimand, the causal effect with reference to a specific target population. Commonly used estimands include the average treatment effect in the treated (ATT), the average treatment effect in the untreated (ATU), the average treatment effect in the population (ATE), and the average treatment effect in the overlap (i.e., equipoise population; ATO). Each estimand has its own assumptions, interpretation, and statistical methods that can be used to estimate it. This article provides guidance on selecting and interpreting an estimand to help medical researchers correctly implement statistical methods used to estimate causal effects in observational studies and to help audiences correctly interpret the results and limitations of these studies. The interpretations of the estimands resulting from regression and instrumental variable analyses are also discussed. Choosing an estimand carefully is essential for making valid inferences from the analysis of observational data and ensuring results are replicable and useful for practitioners.

翻译：观察性研究中的匹配与加权方法涉及估计量的选择，即针对特定目标人群的因果效应。常用估计量包括处理组平均处理效应（ATT）、未处理组平均处理效应（ATU）、总体平均处理效应（ATE）以及重叠人群平均处理效应（即均衡人群，ATO）。每种估计量均有其特定的假设、解释和可用的统计估算方法。本文就如何选择与解读估计量提供指导，以帮助医学研究者正确实施观察性研究中因果效应估计的统计方法，并协助受众准确理解此类研究的结果与局限性。此外还讨论了回归分析与工具变量分析所产生的估计量解读。审慎选择估计量对于从观察性数据分析中得出有效推论、确保结果可重复性及实践适用性至关重要。