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In this work, we investigate the problem of computing an experimental distribution from a combination of the observational distribution and a partial qualitative description of the causal structure of the domain under investigation. This description is given by a partial ancestral graph (PAG) that represents a Markov equivalence class of causal diagrams, i.e., diagrams that entail the same conditional independence model over observed variables, and is learnable from the observational data. Accordingly, we develop a complete algorithm to compute the causal effect of an arbitrary set of intervention variables on an arbitrary outcome set.
|Title of host publication||Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019|
|Publisher||International Joint Conferences on Artificial Intelligence|
|Number of pages||5|
|Publication status||Published - Aug 2019|
|Event||28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China|
Duration: 10 Aug 2019 → 16 Aug 2019
|Name||IJCAI International Joint Conference on Artificial Intelligence|
|Conference||28th International Joint Conference on Artificial Intelligence, IJCAI 2019|
|Period||10/08/19 → 16/08/19|
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- 1 Finished
Causation, Decision, and Imprecise Probabilities
ZHANG, J. & SEIDENFELD, T.
Research Grants Council (HKSAR)
1/01/16 → 31/12/17
Project: Grant Research