Sensitivity Analysis Without Assumptions

By Peng Ding, Tyler J. VanderWeele. Epidemiology, 2015

529 citations recorded by the source corpus.

Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on causal conclusions. However, previous sensitivity analysis approaches often make strong and untestable assumptions such as having an unmeasured confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the outcome, or having only one unmeasured confounder. Without imposing any assumptions on the unmeasured confounder or confounders, we derive a bounding factor and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Our approach is easy to implement and involves only two sensitivity parameters. Surprisingly, our bounding factor, which makes no simplifying assumptions, is no more conservative than a number of previous sensitivity analysis techniques that do make assumptions. Our new bounding factor implies not only the traditional Cornfield conditions that both the relative risk of the exposure on the confounder and that of the confounder on the outcome must satisfy but also a high threshold that the maximum of these relative risks must satisfy. Furthermore, this new bounding factor can be viewed as a measure of the strength of confounding between the exposure and the outcome induced by a confounder.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c0c1d1867589a75835083f5b140cfac62b678327

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Citations in the corpus

Three principles of data science: predictability, computability, and stability (PCS) (2018, background): “Sensitivity analyses test the stability of a causal eﬀect relative to unmeasured confounding (57, 58).”
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results (2018, background): “Causal sensitivity analysis considers if an effect is robust to counterfactual unmeasured confounders from a deﬁned set (Ding and VanderWeele 2016, Liu et al. 2013).”
Sensitivity Analysis in Observational Research: Introducing the E-Value (2017, methodology): “A potential remedy would be to provide a large table with different values of RRUD and RREU, including some which are large, to give readers and researchers a sense as to how sensitive the conclusions are to potential unmeasured confounders (37).”