Add to ORKGAn action rule predicts the actions that should be taken to move an object into a desired state (for instance, in churn prediction, to make a customer happy so he/she does not end his contract). Action rule mining is the task of analyzing a database in order to find action rules. Up till now, approaches to action rule mining ignored the causal structure of the data. In this talk, I argue that the causal structure is relevant, and I discuss recent work on incorporating (estimated) causal relationships into action rule mining.Invited talk at the workshop.status: publishe
Machine learning has traditionally been focused on prediction. Given observations that have been gen...
this paper is to summarize recent advances in causal reasoning, especially those that use causal gra...
The rules we learned from our rule learning framework. It can also be seen as a causal knowledge gr...
In many business contexts, the ultimate goal of knowledge discovery is not the knowledge itself, but...
Actionable Knowledge Discovery has attracted much interest lately. It is almost a new paradigm shift...
much interest lately. It is almost a new paradigm shift toward mining more usable and more applicabl...
Knowledge discovery and data mining provide an array of solutions for real-world problems. When faci...
Most `causal' approaches to reasoning about action have not addressed the basic question of causalit...
The goal of this paper is the formulation of a well-founded solution to the ramification problem: th...
Causal reasoning has become very important in the last few years in logic based action theories. In ...
Most `causal' approaches to reasoning about action have not addressed the basic question of cau...
AbstractCommonsense causal discourse requires a language with which to express varying degrees of ca...
In the reasoning about actions community, causal relationships have been proposed as a possible appr...
Introduction Reasoning in terms of cause and effect is a strategy that arises in many tasks. For ex...
This article explores the combined application of inductive learning algorithms and causal inference...
Machine learning has traditionally been focused on prediction. Given observations that have been gen...
this paper is to summarize recent advances in causal reasoning, especially those that use causal gra...
The rules we learned from our rule learning framework. It can also be seen as a causal knowledge gr...
In many business contexts, the ultimate goal of knowledge discovery is not the knowledge itself, but...
Actionable Knowledge Discovery has attracted much interest lately. It is almost a new paradigm shift...
much interest lately. It is almost a new paradigm shift toward mining more usable and more applicabl...
Knowledge discovery and data mining provide an array of solutions for real-world problems. When faci...
Most `causal' approaches to reasoning about action have not addressed the basic question of causalit...
The goal of this paper is the formulation of a well-founded solution to the ramification problem: th...
Causal reasoning has become very important in the last few years in logic based action theories. In ...
Most `causal' approaches to reasoning about action have not addressed the basic question of cau...
AbstractCommonsense causal discourse requires a language with which to express varying degrees of ca...
In the reasoning about actions community, causal relationships have been proposed as a possible appr...
Introduction Reasoning in terms of cause and effect is a strategy that arises in many tasks. For ex...
This article explores the combined application of inductive learning algorithms and causal inference...
Machine learning has traditionally been focused on prediction. Given observations that have been gen...
this paper is to summarize recent advances in causal reasoning, especially those that use causal gra...
The rules we learned from our rule learning framework. It can also be seen as a causal knowledge gr...