Add to ORKGThe sufficient cause framework describes how sets of sufficient causes are responsible for causing some event or outcome. It is known that it is closely connected with Boolean functions. In this paper we define this relation formally, and show how it can be used together with Fourier expansion of the Boolean functions to lead to new insights. The main result is a probibalistic version of the multifactor potential outcome model based on independence of causal influence models and Bayesian networks.QC 20190925</p
This article considers the extent to which Bayesian networks with imprecise probabilities, which are...
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A...
Abstract We clarify the status of the so-called causal minimality condition in the theory of causal ...
The sufficient cause framework describes how sets of sufficient causes are responsible for causing s...
peer reviewedThis paper investigates the use of Boolean techniques in a systematic study of cause-ef...
The theory of causal independence is frequently used to facilitate the assessment of the probabilist...
AbstractIndependence of causal influence (ICI) offer a high level starting point for the design of B...
Contains fulltext : 139687.pdf (preprint version ) (Open Access
this paper another misguided attempt to reduce causation to probability. But causation leaves a dist...
I advance a new theory of causal relevance, according to which causal claims convey information abou...
AbstractIn this article, we demonstrate the usefulness of causal Bayesian networks as probabilistic ...
The paper displays the similarity between the theory of probabilistic causation developed by Glymour...
I present a formalism that combines two methodologies: *objective Bayesianism* and *Bayesian nets*. ...
AbstractTo specify a Bayesian network (BN), a conditional probability table (CPT), often of an effec...
Abstract- Causal reasoning occupies a central position in human reasoning. In many ways, causality i...
This article considers the extent to which Bayesian networks with imprecise probabilities, which are...
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A...
Abstract We clarify the status of the so-called causal minimality condition in the theory of causal ...
The sufficient cause framework describes how sets of sufficient causes are responsible for causing s...
peer reviewedThis paper investigates the use of Boolean techniques in a systematic study of cause-ef...
The theory of causal independence is frequently used to facilitate the assessment of the probabilist...
AbstractIndependence of causal influence (ICI) offer a high level starting point for the design of B...
Contains fulltext : 139687.pdf (preprint version ) (Open Access
this paper another misguided attempt to reduce causation to probability. But causation leaves a dist...
I advance a new theory of causal relevance, according to which causal claims convey information abou...
AbstractIn this article, we demonstrate the usefulness of causal Bayesian networks as probabilistic ...
The paper displays the similarity between the theory of probabilistic causation developed by Glymour...
I present a formalism that combines two methodologies: *objective Bayesianism* and *Bayesian nets*. ...
AbstractTo specify a Bayesian network (BN), a conditional probability table (CPT), often of an effec...
Abstract- Causal reasoning occupies a central position in human reasoning. In many ways, causality i...
This article considers the extent to which Bayesian networks with imprecise probabilities, which are...
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A...
Abstract We clarify the status of the so-called causal minimality condition in the theory of causal ...