Causal Graphical Models with Latent Variables: Learning and Inference
نویسندگان
چکیده
Several paradigms exist for modeling causal graphical models for discrete variables that can handle latent variables without explicitly modeling them quantitatively. Applying them to a problem domain consists of different steps: structure learning, parameter learning and using them for probabilistic or causal inference. We discuss two well-known formalisms, namely semi-Markovian causal models and maximal ancestral graphs and indicate their strengths and limitations. Previously an algorithm has been constructed that by combining elements from both techniques allows to learn a semi-Markovian causal models from a mixture of observational and experimental data. The goal of this paper is to recapitulate the integral learning process from observational and experimental data and to demonstrate how different types of inference can be performed efficiently in the learned models. We will do this by proposing an alternative representation for semi-Markovian causal models.
منابع مشابه
Experimental Learning of Causal Models with Latent Variables
This article discusses graphical models that can handle latent variables without explicitly modeling them quantitatively. There exist several paradigms for such problem domains. Two of them are semi-Markovian causal models and maximal ancestral graphs. Applying these techniques to a problem domain consists of several steps, typically: structure learning from observational and experimental data,...
متن کاملLearning Semi-Markovian Causal Models using Experiments
Semi-Markovian causal models (SMCMs) are an extension of causal Bayesian networks for modeling problems with latent variables. However, there is a big gap between the SMCMs used in theoretical studies and the models that can be learned from observational data alone. The result of standard algorithms for learning from observations, is a complete partially ancestral graph (CPAG), representing the...
متن کاملLearning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables
While there has been considerable research in learning probabilistic graphical models from data for predictive and causal inference, almost all existing algorithms assume a single dataset of i.i.d. observations for all variables. For many applications, it may be impossible or impractical to obtain such datasets, but multiple datasets of i.i.d. observations for different subsets of these variabl...
متن کاملScaling It Up: Stochastic Search Structure Learning in Graphical Models
Gaussian concentration graph models and covariance graph models are two classes of graphical models that are useful for uncovering latent dependence structures among multivariate variables. In the Bayesian literature, graphs are often determined through the use of priors over the space of positive definite matrices with fixed zeros, but these methods present daunting computational burdens in la...
متن کاملTitle Methods for Graphical Models and Causal Inference
March 19, 2015 Version 2.0-10 Date 2015-03-18 Author Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler Maintainer Markus Kalisch Title Methods for Graphical Models and Causal Inference Description Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational ...
متن کامل