Bounding Sets for Treatment Effects with Proportional Selection
Deepankar Basu ()
Additional contact information
Deepankar Basu: Department of Economics, University of Massachusetts Amherst
UMASS Amherst Economics Working Papers from University of Massachusetts Amherst, Department of Economics
In linear econometric models with proportional selection on unobservables, omitted variable bias in estimated treatment effects are roots of a cubic equation involving estimated parameters from a short and intermediate regression, the former excluding and the latter including all observable controls. The roots of the cubic are functions of delta, the degree of proportional selection on unobservables, and R_max, the R-squared in a hypothetical long regression that includes the unobservable confounder and all observable controls. In this paper a simple method is proposed to compute roots of the cubic over meaningful regions of the delta-R_max plane and use the roots to construct bounding sets for the true treatment effect. The proposed method is illustrated with both a simulated and an observational data set.
Keywords: treatment effect; omitted variable bias (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-isf and nep-ore
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ums:papers:2021-10
Access Statistics for this paper
More papers in UMASS Amherst Economics Working Papers from University of Massachusetts Amherst, Department of Economics Thompson Hall, Amherst, MA 01003. Contact information at EDIRC.
Bibliographic data for series maintained by Daniele Girardi ().