Economists are often interested in the mechanisms by which a particular treatment affects an outcome. This paper develops tests for the ``sharp null of full mediation'' that the treatment $D$ operates on the outcome $Y$ only through a particular conjectured mechanism (or set of mechanisms) $M$. A key observation is that if $D$ is randomly assigned and has a monotone effect on $M$, then $D$ is a valid instrumental variable for the local average treatment effect (LATE) of $M$ on $Y$. Existing tools for testing the validity of the LATE assumptions can thus be used to test the sharp null of full mediation when $M$ and $D$ are binary. We develop a more general framework that allows one to test whether the effect of $D$ on $Y$ is fully explained by a potentially multi-valued and multi-dimensional set of mechanisms $M$, allowing for relaxations of the monotonicity assumption. We further provide methods for lower-bounding the size of the alternative mechanisms when the sharp null is rejected. An advantage of our approach relative to existing tools for mediation analysis is that it does not require stringent assumptions about how $M$ is assigned; on the other hand, our approach helps to answer different questions than traditional mediation analysis by focusing on the sharp null rather than estimating average direct and indirect effects. We illustrate the usefulness of the testable implications in two empirical applications.

翻译：经济学家通常关注特定处理影响结果的机制。本文针对"完全中介的尖锐原假设"开发了检验方法，该假设认为处理变量$D$仅通过某一特定推断机制（或机制集）$M$影响结果变量$Y$。关键发现是：若$D$被随机分配且对$M$具有单调效应，则$D$可作为$M$对$Y$局部平均处理效应（LATE）的有效工具变量。因此，当$M$与$D$为二元变量时，现有LATE假设有效性检验工具可直接用于检验完全中介的尖锐原假设。我们建立了更通用的框架，允许检验$D$对$Y$的效应是否完全由可能多值且多维的机制集$M$解释，并放宽了单调性假设。进一步地，本文提供了当尖锐原假设被拒绝时，替代机制规模下界的估计方法。相较于现有中介分析方法，本方法的优势在于无需对$M$的分配方式施加严格假设；另一方面，通过聚焦于尖锐原假设而非估计平均直接效应与间接效应，本方法可回答与传统中介分析不同的问题。我们通过两项实证应用展示了可检验推论的有效性。