信号、随机分配和因果效应估计

Gilles Chemla, C. Hennessy
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引用次数: 2

摘要

随机分配的因果证据被贴上了“最可信”的标签。我们认为,在金融/经济学中,它通常是不完整的,忽略了真正的实证因果链的核心部分。随机分配在消除自我选择的同时,也排除了通过治疗选择发出信号的可能性。然而,在实验之外,代理人享有自由裁量权来发出信号,从而导致信念和结果的变化。因此,如果目标是告知自由决定,而不是预测强制/错误行动后的结果,那么随机化就存在问题。如上所示,信号可以放大、减弱或逆转因果效应的迹象。因此,传统的实证金融方法,如事件研究,往往更可信/有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Signaling, Random Assignment, and Causal Effect Estimation
Causal evidence from random assignment has been labeled "the most credible." We argue it is generally incomplete in finance/economics, omitting central parts of the true empirical causal chain. Random assignment, in eliminating self-selection, simultaneously precludes signaling via treatment choice. However, outside experiments, agents enjoy discretion to signal, thereby causing changes in beliefs and outcomes. Therefore, if the goal is informing discretionary decisions, rather than predicting outcomes after forced/mistaken actions, randomization is problematic. As shown, signaling can amplify, attenuate, or reverse signs of causal effects. Thus, traditional methods of empirical finance, e.g. event studies, are often more credible/useful.
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