# Causal Graphical Models with Latent Variables: Learning and Inference

@inproceedings{Meganck2007CausalGM, title={Causal Graphical Models with Latent Variables: Learning and Inference}, author={Stijn Meganck and Philippe Leray and Bernard Manderick}, booktitle={ECSQARU}, year={2007} }

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…

## 14 Citations

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This article proposes a new method for learning CBNs from observational data and interventions by adding a new step based on the integration of ontological knowledge, which will allow us to choose efficiently the interventions to perform in order to obtain the complete CBN.

### Probabilistic matching: Causal inference under measurement errors

- Computer Science2017 International Joint Conference on Neural Networks (IJCNN)
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### Inferring interventions in product-based possibilistic causal networks

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A method that leverages the assignment of multiple treatments over time to enable the estimation of treatment effects in the presence of multi-cause hidden confounders and a theoretical analysis for obtaining unbiased causal effects of time-varying exposures using the Time Series Deconfounder.

### Machine learning approaches to statistical dependences and causality Dagstuhl Seminar

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- 2010

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- Mathematics, Computer ScienceUAI
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This paper presents a factorization criterion for cyclic directed mixed graphs that is equivalent to the global Markov property given by (the natural extension of) dseparation.

### Towards an Integral Approach for Modeling Causality

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- 2008

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not, and towards an Integral Approach for Modeling Causality Stijn Meganck.

## References

SHOWING 1-10 OF 35 REFERENCES

### Learning Semi-Markovian Causal Models using Experiments

- Computer ScienceProbabilistic Graphical Models
- 2006

This paper provides a set of rules that indicate which experiments are needed in order to transform a CPAG to a completely oriented SMCM and how the results of these experiments have to be processed and shows how this parametrisation can be used to develop methods to efficiently perform both probabilistic and causal inference.

### A Tutorial on Learning with Bayesian Networks

- Computer ScienceInnovations in Bayesian Networks
- 2008

Methods for constructing Bayesian networks from prior knowledge are discussed and methods for using data to improve these models are summarized, including techniques for learning with incomplete data.

### Active Learning for Structure in Bayesian Networks

- Computer ScienceIJCAI
- 2001

Experimental results show that active learning can substantially reduce the number of observations required to determine the structure of a domain.

### An Introduction to Variational Methods for Graphical Models

- Computer ScienceMachine Learning
- 2004

This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields), and describes a general framework for generating variational transformations based on convex duality.

### Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach

- Computer ScienceMDAI
- 2006

An algorithm is introduced that allows to actively add results of experiments so that arcs can be directed during learning and it is shown that this approach allows to learn a causal Bayesian network optimally with relation to a number of decision criteria.

### Causal Inference and Reasoning in Causally Insu-cient Systems

- Computer Science
- 2006

This dissertation shows that the FCI algorithm, a sound inference procedure in the literature for inferring features of the unknown causal structure from facts of probabilistic independence and dependence, is complete in the sense that any feature of the causal structure left undecided by the inference procedure is indeed underdetermined by facts of Probabilistic Independence and dependence.

### Causal Discovery from a Mixture of Experimental and Observational Data

- Computer ScienceUAI
- 1999

The learning method was applied to predict the causal structure and to estimate the causal parameters that exist among randomly selected pairs of nodes in ALARM that are not confounded.

### On the Testable Implications of Causal Models with Hidden Variables

- Computer ScienceUAI
- 2002

This paper offers a systematic way of identifying functional constraints and facilitates the task of testing causal models as well as inferring such models from data.

### A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables

- Mathematics, Computer ScienceUAI
- 2005

This paper establishes a transformational characterization of Markov equivalence for directed MAGs, which it is expected will have similar uses as it does for DAGs.

### Markov Equivalence Classes for Maximal Ancestral Graphs

- MathematicsUAI
- 2002

A join operation is defined on ancestral graphs which will associate a unique graph with a Markov equivalence class, thereby facilitating model search and providing a proof of the pairwise Markov property for joined ancestral graphs.