Some Staged Tree Models For Learning From Interventions

          @misc{ scivideos_19670,
            doi = {},
            url = {https://simons.berkeley.edu/talks/some-staged-tree-models-learning-interventions},
            author = {},
            keywords = {},
            language = {en},
            title = {Some Staged Tree Models For Learning From Interventions},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {feb},
            note = {19670 see, \url{https://scivideos.org/Simons-Institute/19670}}
          }
          

Abstract

A well-known limitation of modeling causal systems via DAGs is their inability to encode context-specific information. Among the several proposed representations for context-specific causal information are the staged tree models, which are colored probability trees capable of expressing highly diverse context-specific information. The expressive power of staged trees comes at the cost of easy interpretability and the admittance of desirable properties useful in the development of causal discovery algorithms. In this talk, we consider a subfamily of staged trees, which we call CStrees, that admit an alternative representation via a sequence of DAGs. This alternate representation allows us to prove a Verma-Pearl-type characterization of model equivalence for CStrees which extends to the interventional setting, providing a graphical characterization of interventional CStree model equivalence. We will discuss these results and their potential applications to causal discovery algorithms for context-specific models based on interventional and observational data.