Influence Diagrams for Causal Modelling and Inference
- 1 August 2002
- journal article
- Published by Wiley in International Statistical Review
- Vol. 70 (2) , 161-189
- https://doi.org/10.1111/j.1751-5823.2002.tb00354.x
Abstract
Summary: We consider a variety of ways in which probabilistic and causal models can be represented in graphical form. By adding nodes to our graphs to represent parameters, decision,etc., we obtain a generalisation of influence diagrams that supports meaningful causal modelling and inference, and only requires concepts and methods that are already standard in the purely probabilistic case. We relate our representations to others, particularly functional models, and present arguments and examples in favour of their superiority.Keywords
This publication has 20 references indexed in Scilit:
- Chain Graph Models and their Causal InterpretationsJournal of the Royal Statistical Society Series B: Statistical Methodology, 2002
- Causal Inference from Graphical ModelsPublished by Taylor & Francis ,2000
- Causal Inference Without CounterfactualsJournal of the American Statistical Association, 2000
- Causal Inference without CounterfactualsJournal of the American Statistical Association, 2000
- Causal Diagrams for Empirical ResearchBiometrika, 1995
- [Bayesian Analysis in Expert Systems]: Comment: Graphical Models, Causality and InterventionStatistical Science, 1993
- Independence properties of directed markov fieldsNetworks, 1990
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effectMathematical Modelling, 1986
- Conditional Independence for Statistical OperationsThe Annals of Statistics, 1980
- Bayesian Inference for Causal Effects: The Role of RandomizationThe Annals of Statistics, 1978