Sensemakr: Sensitivity Analysis Tools for OLS in R and Stata

28 Pages Posted: 28 May 2020 Last revised: 29 May 2020

See all articles by Carlos Cinelli

Carlos Cinelli

University of Washington - Department of Statistics

Jeremy Ferwerda

Dartmouth College

Chad Hazlett


Date Written: April 30, 2020


This paper introduces the package sensemakr for R and Stata, which implements a suite of sensitivity analysis tools for regression models developed in Cinelli and Hazlett (2020a). Given a regression model, sensemakr can compute sensitivity statistics for routine reporting, such as the robustness value, which describes the minimum strength that unobserved confounders need to have to overturn a research conclusion. The package also provides plotting tools that visually demonstrate the sensitivity of point estimates and t-values to hypothetical confounders. Finally, sensemakr implements formal bounds on sensitivity parameters by means of comparison with the explanatory power of observed variables. All these tools are based on the familiar "omitted variable bias" framework, do not require assumptions regarding the functional form of the treatment assignment mechanism nor the distribution of the unobserved confounders, and naturally handle multiple, non-linear confounders. With sensemakr, users can transparently report the sensitivity of their causal inferences to unobserved confounding, thereby enabling a more precise, quantitative debate as to what can be concluded from imperfect observational studies.

Keywords: causal inference, sensitivity analysis, omitted variable bias, robustness value, R, Stata, bounds

JEL Classification: C31, C51, C52

Suggested Citation

Cinelli, Carlos and Ferwerda, Jeremy and Hazlett, Chad, Sensemakr: Sensitivity Analysis Tools for OLS in R and Stata (April 30, 2020). Available at SSRN: or

Carlos Cinelli (Contact Author)

University of Washington - Department of Statistics ( email )

Seattle, WA
United States


Jeremy Ferwerda

Dartmouth College ( email )

Department of Sociology
Hanover, NH 03755
United States

Chad Hazlett

UCLA ( email )

405 Hilgard Ave.
Los Angeles, CA 90095-1472
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics