Instrumental variables (IVs) can be used to provide evidence as to whether a treatment X has a causal effect on Y. Z is a valid instrument if it satisfies the three core IV assumptions of relevance, independence and the exclusion restriction. Even if the instrument satisfies these assumptions, further assumptions are required to estimate 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). Here, we describe the NO Simultaneous Heterogeneity (NOSH) assumption, which requires the heterogeneity in the X-Y causal effect to be independent of both Z and heterogeneity in the Z-X association. We describe the necessary conditions for NOSH to hold, in which case conventional IV methods are consistent for the ACE even if both homogeneity assumptions and NEM are violated. We illustrate these ideas using simulations and by re-examining selected published studies.
翻译:仪器变量(IVs)可用于提供证据,证明一种处理X是否对Y.Z.具有因果关系,如果它符合相关性、独立性和排除限制这三个核心的四类假设,Z.就是一种有效的工具。即使文书符合这些假设,也需要进一步假设来估计X.Y.对Y.的平均因果关系(ACE)。 这方面的充分假设包括:X.对Y.的因果关系的同质性;Z.与X.的同质性;和无效果改变(NEM.)。这里,我们描述了NO同源异异性(NOSH)假设,要求X-Y因果关系的异质性独立于Z-X协会的Z和异质性。我们描述了NOSH.所坚持的必要条件,在这种情况下,即使同质假设和NEM均被违反,常规四方法也与ACE一致。我们用模拟和重新解析选定的已发表的研究来说明这些想法。