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Replication Data for Omitted Variables, Countervailing Effects, and The Possibility of Overadjustment

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for Omitted Variables, Countervailing Effects, and The Possibility of Overadjustment
 
Identifier https://doi.org/10.7910/DVN/BSKHUF
 
Creator Clarke, Kevin
Kenkel, Brenton
Rueda, Miguel
 
Publisher Harvard Dataverse
 
Description The effect of conditioning on an additional covariate on confounding bias depends, in part, on covariates that are unobserved. We characterize the conditions under which the interaction between a covariate that is available for conditioning and one that is not can affect bias. When the confounding effects of two covariates, one of which is observed, are countervailing (in opposite directions), conditioning on the observed covariate can increase bias. We demonstrate this possibility analytically, and then show that these conditions are not rare in actual data. We also consider whether balance tests or sensitivity analysis can be used to justify the inclusion of an additional covariate. Our results indicate that neither provide protection against overadjustment.
 
Subject Social Sciences
 
Contributor Clarke, Kevin