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Partial Identifiability in Discrete Data With Measurement Error
When data contains measurement errors, it is necessary to make assumptions relating the observed, erroneous data to the unobserved true phenomena of interest.
Evaluating Model Robustness and Stability to Dataset Shift
We consider shifts in user defined conditional distributions, allowing some distributions to shift while keeping other portions of the data distribution fixed.
Learning Models from Data with Measurement Error: Tackling Underreporting
In this paper we present a method for estimating the distribution of an outcome given a binary exposure that is subject to underreporting.
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