Working Paper: NBER ID: w19054
Authors: Emily Oster; Ling Zhong; Unika Shrestha; Damian Kozbur; Guillaume Pouliot; David Birke; Angela Li
Abstract: A common heuristic for evaluating robustness of results to omitted variable bias is to look at coefficient movements after inclusion of controls. This heuristic is informative only if selection on observables is proportional to selection on unobservables. I formalize this link, drawing on theory in Altonji, Elder and Taber (2005) and show how, with this assumption, coefficient movements, along with movements in R-squared values, can be used to calculate omitted variable bias. I discuss empirical implementation and describe a formal bounding argument to replace the coefficient movement heuristic. I show two validation exercises suggesting that this bounding argument would perform well empirically. I discuss application of this procedure to a large set of publications in economics, and use evidence from randomized studies to draw guidelines as to appropriate bounding values.
Keywords: Omitted variable bias; Coefficient stability; Robustness; Econometrics; Causal inference
JEL Codes: C01; I1; I12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| omitted variable bias (C20) | coefficient movements (C10) |
| coefficient movements (C10) | omitted variable bias (C20) |
| R-squared values (C29) | omitted variable bias (C20) |
| R-squared values (C29) | coefficient movements (C10) |
| small coefficient movements with small R-squared movements (C32) | more bias (D91) |
| adjustment (F32) | recovery of true effects (C22) |
| adjustment (F32) | separation of true from false associations (C52) |
| education (I29) | wages (J31) |
| maternal behaviors (J16) | child outcomes (J13) |