Add to ORKGA causal graph can be generated from a dataset using a particular causal algorithm, for instance, the PC algorithm or Fast Causal Inference (FCI). This paper provides two contributions in learning causal graphs: an easy way to handle mixed data so that it can be used to learn causal graphs using the PC algorithm/FCI and a method to evaluate the learned graph structure when the true graph is unknown. This research proposes using kernel functions and Kernel Alignment to handle mixed data. The two main steps of this approach are computing a kernel matrix for each variable and calculating a pseudo-correlation matrix using Kernel Alignment. The Kernel Alignment matrix is used as a substitute for the correlation matrix that is the main component ...
One of the common obstacles for learning causal models from data is that high-order conditional inde...
Real-world data often have a complex structure that can be naturally represented with graphs or logi...
We propose a method to quantify the complexity of conditional probability measures by a Hilbert spac...
A causal graph can be generated from a dataset using a particular causal algorithm, for instance, th...
We describe a causal learning method, which employs measuring the strength of statistical dependence...
We describe a causal learning method, which employs measuring the strength of statistical dependence...
Causal graphs are essential tools to find sufficient adjustment sets in observational studies. Subje...
The field of causal learning has grown in the past decade, establishing itself as a major focus in a...
We study the problem of inferring causal graphs from observational data. We are particularly interes...
Publicly available datasets in health science are often large and observational, in contrast to expe...
In the real world all events are connected. There is a hidden network of dependencies that governs b...
We address the problem of causal discovery from data, making use of the recently proposed causal mod...
2. Kernel measures for dependence 3. Kernel measures for conditional dependence 4. Causal inference ...
Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel genera...
We describe a method for causal inference that measures the strength of statistical dependence by th...
One of the common obstacles for learning causal models from data is that high-order conditional inde...
Real-world data often have a complex structure that can be naturally represented with graphs or logi...
We propose a method to quantify the complexity of conditional probability measures by a Hilbert spac...
A causal graph can be generated from a dataset using a particular causal algorithm, for instance, th...
We describe a causal learning method, which employs measuring the strength of statistical dependence...
We describe a causal learning method, which employs measuring the strength of statistical dependence...
Causal graphs are essential tools to find sufficient adjustment sets in observational studies. Subje...
The field of causal learning has grown in the past decade, establishing itself as a major focus in a...
We study the problem of inferring causal graphs from observational data. We are particularly interes...
Publicly available datasets in health science are often large and observational, in contrast to expe...
In the real world all events are connected. There is a hidden network of dependencies that governs b...
We address the problem of causal discovery from data, making use of the recently proposed causal mod...
2. Kernel measures for dependence 3. Kernel measures for conditional dependence 4. Causal inference ...
Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel genera...
We describe a method for causal inference that measures the strength of statistical dependence by th...
One of the common obstacles for learning causal models from data is that high-order conditional inde...
Real-world data often have a complex structure that can be naturally represented with graphs or logi...
We propose a method to quantify the complexity of conditional probability measures by a Hilbert spac...