Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
arXiv:1205.2617
Abstract
We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to model the effects of interventions, and the independence properties encoded in this model can be represented as a directed graph that allows cycles. In addition to discussing inference and sampling with this representation, we give an exponential family parametrization that allows parameter estimation to be stated as a convex optimization problem; we also give a convex relaxation of the task of simultaneous parameter and structure learning using group l1-regularization. The model is evaluated on simulated data and intracellular flow cytometry data.
Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)
References in corpus (6)
- A simple approach for finding the globally optimal Bayesian network structure
- Directed Cyclic Graphical Representations of Feedback Models
- A Discovery Algorithm for Directed Cyclic Graphs
- Identifying Independencies in Causal Graphs with Feedback
- Extending Factor Graphs so as to Unify Directed and Undirected Graphical Models
- A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models
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- Cyclic Causal Discovery from Continuous Equilibrium Data
- Reasoning about Independence in Probabilistic Models of Relational Data
- Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables
- Estimation Rates for Sparse Linear Cyclic Causal Models
- Dependence versus Conditional Dependence in Local Causal Discovery from Gene Expression Data