Bounding,An Accessible Method for Estimating Principal Causal Effects,Examined and Explained |
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| Authors: | Luke Miratrix Jane Furey Avi Feller Todd Grindal Lindsay C Page |
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| Institution: | 1. Harvard Graduate School of Education, Cambridge, MA;2. BIBB: Federal Institute for Vocational Education and Training, Bonn, Germany;3. Goldman School of Public Policy, UC Berkeley, Berkeley, CA;4. Center for Learning and Development, SRI International, Menlo Park, CA;5. School of Education, University of Pittsburgh, Pittsburgh, PA |
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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. |
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| Keywords: | principal stratification Manski bounds Early College High Schools multisite randomized trials noncompliance |
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