In settings that exhibit selection on both levels and gains, marginal treatment effects (MTE) allow us to go beyond local average treatment effects and estimate the whole distribution of effects. In this article, I survey the theory behind MTE and introduce the package mtefe, which uses several estimation methods to fit MTE models. This package provides important improvements and flexibility over existing packages such as margte (Brave and Walstrum, 2014, Stata Journal 14: 191–217) and calculates various treatment-effect parameters based on the results. I illustrate the use of the package with examples.
When estimating local average and marginal treatment effects using instrumental variables (IV), multivalued endogenous treatments are frequently converted to binary measures, supposedly to improve interpretability or policy relevance. Such binarization introduces a violation of the IV exclusion if (i) the IV affects the multivalued treatment within support areas below and/or above the threshold and (ii) such IV-induced changes in the multivalued treatment affect the outcome. We discuss assumptions that satisfy the IV exclusion restriction with a binarized treatment and permit identifying the average effect of (i) the binarized treatment and (ii) unit-level increases in the original multivalued treatment among specific compliers. We derive testable implications of these assumptions and propose tests, which we apply to the estimation of the returns to college graduation instrumented by college proximity.
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