Instrumental variables (IV) analysis is a powerful, but fragile, tool for drawing causal inferences from observational data. Sociologists increasingly turn to this strategy in settings where unmeasured confounding between the treatment and outcome is likely. This paper reviews the assumptions required for IV and the consequences of violating them, focusing on sociological applications. We highlight three methodological problems IV faces: (i) identification bias, an asymptotic bias from assumption violations; (ii) estimation bias, a finite-sample bias that persists even when assumptions hold; and (iii) type-M error, the exaggeration of effect size given statistical significance. In each case, we emphasize how weak instruments exacerbate these problems and make results sensitive to minor violations of assumptions. We survey IV papers from top sociology journals, finding that assumptions often go unstated and robust uncertainty measures are rarely used. We provide a practical checklist to show how IV, despite its fragility, can still be useful when handled with care.

中文翻译：

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小心处理：社会学家使用工具变量进行因果推理的指南


工具变量 (IV) 分析是一种强大但脆弱的工具，用于从观测数据中得出因果推论。在治疗和结果之间可能存在无法衡量的混杂的情况下，社会学家越来越多地转向这种策略。本文回顾了 IV 所需的假设以及违反这些假设的后果，重点关注社会学应用。我们强调 IV 面临的三个方法论问题：（i）识别偏差，即违反假设的渐近偏差； (ii) 估计偏差，即即使假设成立也仍然存在的有限样本偏差； (iii) M 型误差，即在统计显着性下效应量的夸大。在每种情况下，我们都强调薄弱的工具如何加剧这些问题并使结果对轻微违反假设的情况敏感。我们调查了顶级社会学期刊的 IV 论文，发现假设经常没有被阐明，并且很少使用强有力的不确定性度量。我们提供了一个实用的清单来展示 IV，尽管它很脆弱，但在小心处理时仍然有用。