- Title Pages
- List of Contributors
- 1 Why look at causality in the sciences? A manifesto
- 2 Causality, theories and medicine
- 3 Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts
- 4 Causal modelling, mechanism, and probability in epidemiology
- 5 The IARC and mechanistic evidence
- 6 The Russo–Williamson thesis and the question of whether smoking causes heart disease
- 7 Causal thinking
- 8 When and how do people reason about unobserved causes?
- 9 Counterfactual and generative accounts of causal attribution
- 10 The autonomy of psychology in the age of neuroscience
- 11 Turing machines and causal mechanisms in cognitive science
- 12 Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of psychological research methods
- 13 Causal mechanisms in the social realm
- 14 Getting past Hume in the philosophy of social science
- 15 Causal explanation: Recursive decompositions and mechanisms
- 16 Counterfactuals and causal structure
- 17 The error term and its interpretation in structural models in econometrics
- 18 A comprehensive causality test based on the singular spectrum analysis
- 19 Mechanism schemas and the relationship between biological theories
- 20 Chances and causes in evolutionary biology: How many chances become one chance
- 21 Drift and the causes of evolution
- 22 In defense of a causal requirement on explanation
- 23 Epistemological issues raised by research on climate change
- 24 Explicating the notion of ‘causation’: The role of extensive quantities
- 25 Causal completeness of probability theories — Results and open problems
- 26 Causality Workbench
- 27 When are graphical causal models not good models?
- 28 Why making Bayesian networks objectively Bayesian makes sense
- 29 Probabilistic measures of causal strength
- 30 A new causal power theory
- 31 Multiple testing of causal hypotheses
- 32 Measuring latent causal structure
- 33 The structural theory of causation
- 34 Defining and identifying the effect of treatment on the treated
- 35 Predicting ‘It will work for us’: (Way) beyond statistics
- 36 The idea of mechanism
- 37 Singular and general causal relations: A mechanist perspective
- 38 Mechanisms are real and local
- 39 Mechanistic information and causal continuity
- 40 The causal‐process‐model theory of mechanisms
- 41 Mechanisms in dynamically complex systems
- 42 Third time's a charm: Causation, science and Wittgensteinian pluralism
- Index

# When are graphical causal models not good models?

# When are graphical causal models not good models?

- Chapter:
- (p.562) 27 When are graphical causal models not good models?
- Source:
- Causality in the Sciences
- Author(s):
### Jan Lemeire

### Kris Steenhaut

### Abdellah Touhafi

- Publisher:
- Oxford University Press

The principle of Kolmogorov minimal sufficient statistic (KMSS) states that the meaningful information of data is given by the regularities in the data. The KMSS is the minimal model that describes the regularities. The meaningful information given by a Bayesian network is the directed acyclic graph (DAG) which describes a decomposition of the joint probability distribution into conditional probability distributions (CPDs). If the description given by the Bayesian network is incompressible, the DAG is the KMSS and is faithful. The chapter proves that if a faithful Bayesian network exists, it is the minimal Bayesian network. Moreover, if a Bayesian network gives the KMSS, modularity of the CPDs is the most plausible hypothesis, from which the causal interpretation follows. On the other hand, if the minimal Bayesian network is compressible and is thus not the KMSS, the above implications cannot be guaranteed. When the non‐minimality of the description is due to the compressibility of an individual CPD, the true causal model is an element of the set of minimal Bayesian networks and modularity is still plausible. Faithfulness cannot be guaranteed though. When the concatenation of the descriptions of the CPDs is compressible, the true causal model is not necessarily an element of the set of minimal Bayesian networks. Also modularity may become implausible. This suggests that either there is a kind of meta‐mechanism governing some of the mechanisms or a wrong model class is considered.

*Keywords:*
causal models, Bayesian networks, Kolmogorov complexity, Kolmogorov minimal sufficient statistic

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- Title Pages
- List of Contributors
- 1 Why look at causality in the sciences? A manifesto
- 2 Causality, theories and medicine
- 3 Inferring causation in epidemiology: Mechanisms, black boxes, and contrasts
- 4 Causal modelling, mechanism, and probability in epidemiology
- 5 The IARC and mechanistic evidence
- 6 The Russo–Williamson thesis and the question of whether smoking causes heart disease
- 7 Causal thinking
- 8 When and how do people reason about unobserved causes?
- 9 Counterfactual and generative accounts of causal attribution
- 10 The autonomy of psychology in the age of neuroscience
- 11 Turing machines and causal mechanisms in cognitive science
- 12 Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of psychological research methods
- 13 Causal mechanisms in the social realm
- 14 Getting past Hume in the philosophy of social science
- 15 Causal explanation: Recursive decompositions and mechanisms
- 16 Counterfactuals and causal structure
- 17 The error term and its interpretation in structural models in econometrics
- 18 A comprehensive causality test based on the singular spectrum analysis
- 19 Mechanism schemas and the relationship between biological theories
- 20 Chances and causes in evolutionary biology: How many chances become one chance
- 21 Drift and the causes of evolution
- 22 In defense of a causal requirement on explanation
- 23 Epistemological issues raised by research on climate change
- 24 Explicating the notion of ‘causation’: The role of extensive quantities
- 25 Causal completeness of probability theories — Results and open problems
- 26 Causality Workbench
- 27 When are graphical causal models not good models?
- 28 Why making Bayesian networks objectively Bayesian makes sense
- 29 Probabilistic measures of causal strength
- 30 A new causal power theory
- 31 Multiple testing of causal hypotheses
- 32 Measuring latent causal structure
- 33 The structural theory of causation
- 34 Defining and identifying the effect of treatment on the treated
- 35 Predicting ‘It will work for us’: (Way) beyond statistics
- 36 The idea of mechanism
- 37 Singular and general causal relations: A mechanist perspective
- 38 Mechanisms are real and local
- 39 Mechanistic information and causal continuity
- 40 The causal‐process‐model theory of mechanisms
- 41 Mechanisms in dynamically complex systems
- 42 Third time's a charm: Causation, science and Wittgensteinian pluralism
- Index