论文标题
通过仪器变量进行的平均因果效应估计：无同步的异质性假设
Average causal effect estimation via instrumental variables: the no simultaneous heterogeneity assumption
论文作者
论文摘要
背景：可以使用仪器变量（IVS）来提供证据表明治疗X是否对结果Y具有因果影响。即使仪器Z满足了相关性，独立性和排除限制的三个核心IV假设，也需要进一步的假设来确定对Y的平均因果效应（ACE）对y的平均效应（ACE）对y的平均效应。 Z与X的关联中的同质性；并且没有效果修改（NEM）。方法：我们描述了没有同时的异质性（NOSH）假设，这需要X-Y因果效应的异质性平均与Z-X关联中Z和异质性无关（即与不相关）。例如，如果没有X-Y效应和Z-X关联的常见修饰符，并且X-Y效应是加性线性的。我们使用模拟说明了NOSH，并通过重新验证选定的已发表研究。结果：当NOSH成立时，瓦尔德估计等于王牌，即使同质性假设和NEM都违反了 - 因此，我们证明是特殊的案例，因此违反了 - 更强大）。结论：NOSH足以使用IV识别ACE。由于NOSH比ACE识别的现有假设弱，因此这样做可能比以前预期的更合理。
Background: Instrumental variables (IVs) can be used to provide evidence as to whether a treatment X has a causal effect on an outcome Y. Even if the instrument Z satisfies the three core IV assumptions of relevance, independence and the exclusion restriction, further assumptions are required to identify the average causal effect (ACE) of X on Y. Sufficient assumptions for this include: homogeneity in the causal effect of X on Y; homogeneity in the association of Z with X; and no effect modification (NEM). Methods: We describe the NO Simultaneous Heterogeneity (NOSH) assumption, which requires the heterogeneity in the X-Y causal effect to be mean independent of (i.e., uncorrelated with) both Z and heterogeneity in the Z-X association. This happens, for example, if there are no common modifiers of the X-Y effect and the Z-X association, and the X-Y effect is additive linear. We illustrate NOSH using simulations and by re-examining selected published studies. Results: When NOSH holds, the Wald estimand equals the ACE even if both homogeneity assumptions and NEM (which we demonstrate to be special cases of - and therefore stronger than - NOSH) are violated. Conclusions: NOSH is sufficient for identifying the ACE using IVs. Since NOSH is weaker than existing assumptions for ACE identification, doing so may be more plausible than previously anticipated.
