Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical … practice. This paper considers uncertainty over models that impose different identifying assumptions, which, in general, leads … to a mix of point- and set-identified models. We propose performing inference in the presence of such uncertainty by …

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical … practice. This paper considers uncertainty over models that impose different identifying assumptions, which, in general, leads … to a mix of point- and set-identified models. We propose performing inference in the presence of such uncertainty by …

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical … practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix … of point‐ and set‐identified models. We propose performing inference in the presence of such uncertainty by generalizing …

methodology combines simulation, bootstrapping, and metamodeling.The methodology is illustrated through a simulated manufacturing … simulation Input/Output (I/O) data are analyzed through a first-order polynomial metamodel and bootstrapping. A second experiment …

parameters prediction and wrong decision making. In uncertainty condition, there are no appropriate measures for decision making …. In epistemic uncertainty, we are confronted by the lack of data. Therefore, in calculating the system risk, we encounter … uncertainty condition or in case of the lack of knowledge, the selection of an appropriate method will facilitate the decision …