1. Instrumental variable regression (RegIV) provides a means for detecting and correcting parameter bias in causal models. Widely used in economics, recently several papers have highlighted its potential utility for ecological applications. Little attention has thus far been paid to the fact that IV methods can also be implemented within structural equation models (SEMIV). In this paper I present the motivations, requirements and basic procedures for using SEMIV.
2. I first consider causal inference and IVs from the perspective of a randomized experiment with partial control of the cause of interest. I consider common sources of bias, the role of randomization and limits to its capacity to exclude bias. Sources of bias include omitted confounders, reciprocal causation, reverse causation and measurement error, all of which can all be seen as a single problem—endogeneity. The approach to estimating IV models most commonly used in econometric practice, two-stage least squares regression (2SLS), is explained, followed by a brief exposition of the covariance modelling approach used in SEM. Using data from an ecological field experiment, I illustrate the use of the treatment variable as an IV and then illustrate procedures for evaluating candidate variables that might serve as additional IVs.
3. IV methods are shown to be useful for both detecting endogeneity and removing its influences. I illustrate some of the ways that bias can be generated, as well as diagnostic capabilities and means for remedy embedded within SEM. Procedures for screening and evaluating additional IVs reveal valuable lessons regarding the theoretical requirements and empirical standards for IVs.
4. SEMIV provides a useful way to detect and control for bias. I suggest that the use of IVs within the SEM framework can support the simultaneous pursuit of causal inference and explanatory modelling, a common pair of aspirations for ecologists. Moving forward, there is a need for a better understanding of the capabilities of SEMIV and requirements for successful application.

中文翻译：

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结构方程模型中的工具变量方法

1. 工具变量回归 (RegIV) 提供了一种检测和纠正因果模型中参数偏差的方法。广泛用于经济学，最近有几篇论文强调了它在生态应用中的潜在效用。迄今为止，很少有人注意到 IV 方法也可以在结构方程模型 (SEMIV) 中实施。在本文中，我介绍了使用 SEMIV 的动机、要求和基本程序。
2. 我首先从一个随机实验的角度考虑因果推断和 IV，对感兴趣的原因进行部分控制。我考虑了常见的偏见来源、随机化的作用及其排除偏见的能力。偏差的来源包括忽略的混杂因素、相互因果关系、反向因果关系和测量误差，所有这些都可以看作是一个单一的问题——内生性。解释了计量经济学实践中最常用的估计 IV 模型的方法，即两阶段最小二乘回归 (2SLS)，然后简要说明了 SEM 中使用的协方差建模方法。使用生态现场实验的数据，我说明了处理变量作为 IV 的使用，然后说明了评估可能作为附加 IV 的候选变量的程序。
3. IV 方法被证明可用于检测内生性和消除其影响。我说明了产生偏见的一些方式，以及嵌入在 SEM 中的诊断能力和补救方法。筛选和评估额外 IV 的程序揭示了有关 IV 的理论要求和经验标准的宝贵经验。
4. SEMIV 提供了一种检测和控制偏差的有用方法。我建议在 SEM 框架内使用 IV 可以支持同时追求因果推理和解释性建模，这是生态学家的共同愿望。展望未来，需要更好地了解 SEMIV 的功能和成功应用的要求。