Thijs van Ommen

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This paper discusses an alternative to conditioning that may be used when the prob-<lb>ability distribution is not fully specified. It does not require any assumptions (such<lb>as CAR: coarsening at random) on the unknown distribution. The well-known Monty<lb>Hall problem is the simplest scenario where neither naive conditioning nor the CAR<lb>assumption(More)
Decision makers must often base their decisions on incomplete (coarse) data. Recent research has shown that in a wide variety of coarse data problems, minimax optimal strategies can be recognized using a simple probabilistic condition. This paper develops a computational method to find such strategies in special cases, and shows what difficulties may arise(More)
This paper discusses an alternative to conditioning that may be used when the prob-<lb>ability distribution is not fully specified. It does not require any assumptions (such<lb>as CAR: coarsening at random) on the unknown distribution. The well-known Monty<lb>Hall problem is the simplest scenario where neither naive conditioning nor the CAR<lb>assumption(More)
An important goal in both transfer learning and causal inference is to make accurate predictions when the distribution of the test set and the training set(s) differ. Such a distribution shift may happen as a result of an external intervention on the data generating process, causing certain aspects of the distribution to change, and others to remain(More)
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