Add to ORKGDetermining the causal structure of a domain is a key task in the area of Data Mining and Knowledge Discovery.The algorithm proposed by Wallace et al. [15] has demonstrated its strong ability in discovering Linear Causal Models from given data sets. However, some experiments showed that this algorithm experienced difficulty in discovering linear relations with small deviation, and it occasionally gives a negative message length, which should not be allowed. In this paper, a more efficient and precise MML encoding scheme is proposed to describe the model structure and the nodes in a Linear Causal Model. The estimation of different parameters is also derived. Empirical results show that the new algorithm outperformed the previous MML-based al...
This thesis examines causal discovery within datasets, in particular observational datasets where no...
Causal machine learning (ML) algorithms recover graphical structures that tell us something about ca...
Causal machine learning (ML) algorithms recover graphical structures that tell us something about ca...
Discovering a precise causal structure accurately reflecting the given data is one of the most essen...
This paper presents an examination report on the performance of the improved MML based causal model ...
Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goa...
This thesis made outstanding contribution in automating the discovery of linear causal models. It in...
Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goa...
One common drawback in algorithms for learning Linear Causal Models is that they can not deal with i...
This paper presents an ensemble MML approach for the discovery of causal models. The component learn...
Weak causal relationships and small sample size pose two significant difficulties to the automatic d...
One major difficulty frustrating the application of linear causal models is that they are not easily...
This paper presents a Minimal Causal Model Inducer that can be used for the reliable knowledge disco...
Discovering statistical representations and relations among random variables is a very important tas...
This paper is concerned with the problem of making causal inferences from observational data, when t...
This thesis examines causal discovery within datasets, in particular observational datasets where no...
Causal machine learning (ML) algorithms recover graphical structures that tell us something about ca...
Causal machine learning (ML) algorithms recover graphical structures that tell us something about ca...
Discovering a precise causal structure accurately reflecting the given data is one of the most essen...
This paper presents an examination report on the performance of the improved MML based causal model ...
Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goa...
This thesis made outstanding contribution in automating the discovery of linear causal models. It in...
Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goa...
One common drawback in algorithms for learning Linear Causal Models is that they can not deal with i...
This paper presents an ensemble MML approach for the discovery of causal models. The component learn...
Weak causal relationships and small sample size pose two significant difficulties to the automatic d...
One major difficulty frustrating the application of linear causal models is that they are not easily...
This paper presents a Minimal Causal Model Inducer that can be used for the reliable knowledge disco...
Discovering statistical representations and relations among random variables is a very important tas...
This paper is concerned with the problem of making causal inferences from observational data, when t...
This thesis examines causal discovery within datasets, in particular observational datasets where no...
Causal machine learning (ML) algorithms recover graphical structures that tell us something about ca...
Causal machine learning (ML) algorithms recover graphical structures that tell us something about ca...