Making sense of sensitivity: extending omitted variable bias

By Carlos Cinelli, Chad Hazlett. Journal of The Royal Statistical Society Series B-statistical Methodology, 2019

949 citations recorded by the source corpus.

We extend the omitted variable bias framework with a suite of tools for sensitivity analysis in regression models that does not require assumptions on the functional form of the treatment assignment mechanism nor on the distribution of the unobserved confounders, naturally handles multiple confounders, possibly acting non‐linearly, exploits expert knowledge to bound sensitivity parameters and can be easily computed by using only standard regression results. In particular, we introduce two novel sensitivity measures suited for routine reporting. The robustness value describes the minimum strength of association that unobserved confounding would need to have, both with the treatment and with the outcome, to change the research conclusions. The partial R2 of the treatment with the outcome shows how strongly confounders explaining all the residual outcome variation would have to be associated with the treatment to eliminate the estimated effect. Next, we offer graphical tools for elaborating on problematic confounders, examining the sensitivity of point estimates and t‐values, as well as ‘extreme scenarios’. Finally, we describe problems with a common ‘benchmarking’ practice and introduce a novel procedure to bound the strength of confounders formally on the basis of a comparison with observed covariates. We apply these methods to a running example that estimates the effect of exposure to violence on attitudes toward peace.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dea4f30d71daf52d5ff24bdc6b2d7cb2fedb3986

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Citations in the corpus

On the Nuisance of Control Variables in Regression Analysis (2020, background): “That is because Z1 is correlated with Z2 and will thus partially pick up an effect of Z2 on Y too (Cinelli and Hazlett, 2020).”
Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing (2021, methodology): “Caveats to this approach and more sophisticated empirical calibration strategies are discussed in [21, 59, 11].”