Sharp bounds on the relative treatment effect for ordinal outcomes

2019/07/24 by Lu, Jiannan, Zhang, Yunshu, Ding, Peng · 1 citation
#Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.1907.10287

Abstract

For ordinal outcomes, the average treatment effect is often ill-defined and hard to interpret. Echoing Agresti and Kateri (2017), we argue that the relative treatment effect can be a useful measure especially for ordinal outcomes, which is defined as γ= pr\ Y_i(1) > Y_i(0) \ - pr\ Y_i(1) < Y_i(0) \, with Y_i(1) and Y_i(0) being the potential outcomes of unit i under treatment and control, respectively. Given the marginal distributions of the potential outcomes, we derive the sharp bounds on γ, which are identifiable parameters based on the observed data. Agresti and Kateri (2017) focused on modeling strategies under the assumption of independent potential outcomes, but we allow for arbitrary dependence.

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