Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding

Peña, Jose M.

arXiv.org Machine Learning 

We report assumption-free bounds for any contrast between the probabilities of the potential outcome under exposure and non-exposure when the confounders are missing not at random. We assume that the missingness mechanism is outcome-independent. We also report a sensitivity analysis method to complement our bounds.

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