Bounding, An Accessible Method for Estimating Principal Causal Effects, Examined and Explained.
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| Title: | Bounding, An Accessible Method for Estimating Principal Causal Effects, Examined and Explained. |
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| Authors: | Miratrix, Luke1 (AUTHOR) luke_miratrix@gse.harvard.edu, Furey, Jane2 (AUTHOR), Feller, Avi3 (AUTHOR), Grindal, Todd4 (AUTHOR), Page, Lindsay C.5 (AUTHOR) |
| Source: | Journal of Research on Educational Effectiveness. Jan-Mar2018, Vol. 11 Issue 1, p133-162. 30p. |
| Subject Terms: | *Education |
| Abstract: | Estimating treatment effects for subgroups defined by posttreatment behavior (i.e., estimating causal effects in a principal stratification framework) can be technically challenging and heavily reliant on strong assumptions. We investigate an alternative path: using bounds to identify ranges of possible effects that are consistent with the data. This simple approach relies on fewer assumptions and yet can result in policy-relevant findings. As we show, even moderately predictive covariates can be used to substantially tighten bounds in a straightforward manner. Via simulation, we demonstrate which types of covariates are maximally beneficial. We conclude with an analysis of a multisite experimental study of Early College High Schools. When examining the program's impact on students completing the ninth grade “on-track” for college, we find little impact for ECHS students who would otherwise attend a high-quality high school, but substantial effects for those who would not. This suggests a potential benefit in expanding these programs in areas primarily served by lower quality schools. [ABSTRACT FROM PUBLISHER] |
| Copyright of Journal of Research on Educational Effectiveness is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Abstract: | Estimating treatment effects for subgroups defined by posttreatment behavior (i.e., estimating causal effects in a principal stratification framework) can be technically challenging and heavily reliant on strong assumptions. We investigate an alternative path: using bounds to identify ranges of possible effects that are consistent with the data. This simple approach relies on fewer assumptions and yet can result in policy-relevant findings. As we show, even moderately predictive covariates can be used to substantially tighten bounds in a straightforward manner. Via simulation, we demonstrate which types of covariates are maximally beneficial. We conclude with an analysis of a multisite experimental study of Early College High Schools. When examining the program's impact on students completing the ninth grade “on-track” for college, we find little impact for ECHS students who would otherwise attend a high-quality high school, but substantial effects for those who would not. This suggests a potential benefit in expanding these programs in areas primarily served by lower quality schools. [ABSTRACT FROM PUBLISHER] |
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| ISSN: | 19345747 |
| DOI: | 10.1080/19345747.2017.1379576 |
