Causal Bayesian Networks, Signalling Games and Implicature of ‘More Than n’

2015 ◽  
pp. 25-42
Author(s):  
Anton Benz
Keyword(s):  
Bayesian Networks ◽  
Causal Bayesian Networks ◽  
Signalling Games

scholarly journals Provable Guarantees on the Robustness of Decision Rules to Causal Interventions

2021 ◽  
Author(s):  
Benjie Wang ◽  
Clare Lyle ◽  
Marta Kwiatkowska
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Lower Bounds ◽  
Decision Rules ◽  
Experimental Results ◽  
Upper And Lower Bounds ◽  
Worst Case ◽  
Data Generating Process ◽  
Causal Bayesian Networks ◽  
Novel Model

Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems. Such shifts can be viewed as interventions on a causal graph, which capture (possibly hypothetical) changes in the data-generating process, whether due to natural reasons or by the action of an adversary. We consider causal Bayesian networks and formally define the interventional robustness problem, a novel model-based notion of robustness for decision functions that measures worst-case performance with respect to a set of interventions that denote changes to parameters and/or causal influences. By relying on a tractable representation of Bayesian networks as arithmetic circuits, we provide efficient algorithms for computing guaranteed upper and lower bounds on the interventional robustness probabilities. Experimental results demonstrate that the methods yield useful and interpretable bounds for a range of practical networks, paving the way towards provably causally robust decision-making systems.

Causal Bayesian Networks for Medical Diagnosis: A Case Study in Rheumatoid Arthritis

2020 ◽  
Author(s):  
Ali Fahmi ◽  
Amy MacBrayne ◽  
Evangelia Kyrimi ◽  
Scott McLachlan ◽  
Frances Humby ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Bayesian Networks ◽  
Medical Diagnosis ◽  
Causal Bayesian Networks

Causal Strength

2019 ◽  
pp. 155-184
Author(s):  
Jan Sprenger ◽  
Stephan Hartmann
Keyword(s):  
Bayesian Networks ◽  
Outcome Measures ◽  
Probabilistic Theory ◽  
Specific Measure ◽  
Bayesian Confirmation Theory ◽  
Bayesian Confirmation ◽  
Difference Making ◽  
Causal Bayesian Networks ◽  
The Difference ◽  
Causal Strength

The question “When is C a cause of E?” is well-studied in philosophy—much more than the equally important issue of quantifying the causal strength between C and E. In this chapter, we transfer methods from Bayesian Confirmation Theory to the problem of explicating causal strength. We develop axiomatic foundations for a probabilistic theory of causal strength as difference-making and proceed in three steps: First, we motivate causal Bayesian networks as an adequate framework for defining and comparing measures of causal strength. Second, we demonstrate how specific causal strength measures can be derived from a set of plausible adequacy conditions (method of representation theorems). Third, we use these results to argue for a specific measure of causal strength: the difference that interventions on the cause make for the probability of the effect. An application to outcome measures in medicine and discussion of possible objections concludes the chapter.

scholarly journals Non-Invasive Clinical Parameters for the Prediction of Urodynamic Bladder Outlet Obstruction: Analysis Using Causal Bayesian Networks

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e113131 ◽  
Author(s):  
Myong Kim ◽  
Abhilash Cheeti ◽  
Changwon Yoo ◽  
Minsoo Choo ◽  
Jae-Seung Paick ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Bladder Outlet Obstruction ◽  
Outlet Obstruction ◽  
Clinical Parameters ◽  
Non Invasive ◽  
Causal Bayesian Networks
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