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

Name	File Type	Size	Last Modified
Data			09/30/2019 03:47:PM
Output			09/30/2019 03:50:PM
01-Dataset-Creation.do	text/x-stata-syntax	11.8 KB	09/30/2019 11:47:AM
02-Analysis.do	text/x-stata-syntax	6.9 KB	09/30/2019 11:50:AM
03-Mathematica-Supplement.nb	text/plain	44.6 KB	09/30/2019 11:39:AM
ElwertPfeffer2019.pdf	application/pdf	835.7 KB	09/14/2019 06:03:PM
README.txt	text/plain	744 bytes	09/30/2019 11:49:AM

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:

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: PSID

Geographic Coverage: United States

Time Period(s): 1968 – 1992

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Published Versions

V2 [2019-09-30]

V1 [2018-07-25]

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