Unmeasured confounding factors in observational data may lead to biased estimates of the effect of an exposure on an outcome. Consequently, a central problem in causal inference based on observational data is sensitivity analysis with respect to unmeasured confounding. Existing sensitivity-analysis methods generally focus on worst-case bounds. We propose an exact method for quantifying the significance of confounding, defined here in terms of the complete range of analytical values of the stratification-based Risk Ratio over the set of all joint distributions compatible with the observed characteristics. We refer to the proposed method as Inverse Confounding Analysis (ICA) - it is described in [1]. This paper presents examples of applying ICA to several real-world statistical datasets and examines an important issue that has attracted criticism of the method: the practical feasibility of obtaining the input data required for its application. The results are compared with those of two other methods for confounding analysis, the E-value and the method proposed by Pena.

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