存在不依从情形的随机实验中的稳健识别
Robust Identification in Randomized Experiments with Noncompliance
AI总结：
本文针对存在不依从的随机实验，在两组放松的LATE假设下推导了因果参数的紧界，并通过实证应用验证了标准LATE假设的不兼容性，提供了更具稳健性的因果识别方案。
摘要：
工具变量（IV）方法被广泛应用于实证研究中，以识别政策的因果效应。在局部平均处理效应（LATE）框架下，IV估计量在三个核心假设下可识别LATE：随机分配、排他性约束和单调性。然而，这些假设在许多应用场景中常受到质疑，导致部分研究者对IV估计量的因果解释存疑。本文在标准LATE假设可能被违背的、存在不依从的随机实验场景下，研究因果参数的稳健识别问题。我们讨论了两组更弱假设下的识别情况：仅保留随机分配与排他性约束（不要求单调性），以及仅保留随机分配与单调性（不要求排他性约束）。我们推导了在这两组放松的LATE假设下，部分因果参数的紧界。最后，我们将所提方法应用于重新检验Bursztyn、González与Yanagizawa-Drott（2020）开展的随机信息实验，发现标准LATE假设在该应用中无法同时成立，随后我们估计了两组放松假设下的稳健识别集。
英文摘要：
Instrument variable (IV) methods are widely used in empirical research to identify causal effects of a policy. In the local average treatment effect (LATE) framework, the IV estimand identifies the LATE under three main assumptions: random assignment, exclusion restriction, and monotonicity. However, these assumptions are often questionable in many applications, leading some researchers to doubt the causal interpretation of the IV estimand. This paper considers a robust identification of causal parameters in a randomized experiment setting with noncompliance where the standard LATE assumptions could be violated. We discuss identification under two sets of weaker assumptions: random assignment and exclusion restriction (without monotonicity), and random assignment and monotonicity (without exclusion restriction). We derive sharp bounds on some causal parameters under these two sets of relaxed LATE assumptions. Finally, we apply our method to revisit the random information experiment conducted in Bursztyn, González, and Yanagizawa-Drott (2020) and find that the standard LATE assumptions are jointly incompatible in this application. We then estimate the robust identified sets under the two sets of relaxed assumptions.
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