The Future Strikes Back. Using Future Treatments to Detect and Reduce Hidden Bias
Principal Investigator(s): View help for Principal Investigator(s) Felix Elwert, University of Wisconsin-Madison; Fabian Pfeffer, University of Michigan
Version: View help for Version V2
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Project Citation:
Elwert, Felix, and Pfeffer, Fabian. The Future Strikes Back. Using Future Treatments to Detect and Reduce Hidden Bias. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-09-30. https://doi.org/10.3886/E104060V2
Project Description
Summary:
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Conventional advice discourages controlling for post-outcome variables in regression
analysis. By contrast, we show that controlling for commonly available post-outcome (i.e. future)
values of the treatment variable can help detect, reduce, and even remove omitted variable bias
(unobserved confounding). The premise is that the same unobserved confounder that affects
treatment also affects the future value of the treatment. Future treatments thus proxy for the
unmeasured confounder, and researchers can exploit these proxy measures productively. We
establish several new results: Regarding a commonly assumed data-generating process
involving future treatments, we (1) introduce a simple new approach and show that it strictly
reduces bias; (2) elaborate on existing approaches and show that they can increase bias; (3)
assess the relative merits of alternative approaches; (4) analyze true state dependence and
selection as key challenges. (5) Importantly, we also introduce a new non-parametric test that
uses future treatments to detect hidden bias even when future-treatment estimation fails to
reduce bias. We illustrate these results empirically with an analysis of the effect of parental
income on children’s educational attainment.
Scope of Project
Subject Terms:
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PSID
Geographic Coverage:
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United States
Time Period(s):
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1968 – 1992
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