The causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible distributions as apparently compatible with the given graph. In 10.1515/jci-2017-0020, one of us introduced the inflation technique for formulating useful relaxations of the causal compatibility problem in terms of linear programming. In this work, we develop a formal hierarchy of such causal compatibility relaxations. We prove that inflation i...
Estimating causal relations between two or more variables is an important topic in psychology. Estab...
The aim of this paper is to identify and to characterize the features that render one class of the C...
In this work we propose a statistical approach to handling sources of theoretical uncertainty in str...
The problem of causal inference is to determine if a given probability distribution on observed vari...
Inflation is a Python package that implements inflation algorithms for causal inference. In causal i...
A causal structure is a description of the functional dependencies between random variables. A distr...
We introduce Inflation, a Python library for assessing whether an observed probability distribution ...
Inferring the causal structure that links n observables is usually based upon detecting statistical ...
Different resampling methods for the null hypothesis of no Granger causality are assessed in the set...
yz Causal discovery, for the most part, is concerned with learning causal models in the form of dire...
When performing causal discovery, assumptions have to be made on how the true causal mechanism corre...
Different resampling methods for the null hypothesis of no Granger causality are assessed in the set...
Scientists aim to design experiments and analyze evidence to obtain maximum knowledge. Although scie...
Over the past twenty-five years, a large number of algorithms have been developed to learn the struc...
The current paper develops a probabilistic theory of causation using measure-theoretical concepts an...
Estimating causal relations between two or more variables is an important topic in psychology. Estab...
The aim of this paper is to identify and to characterize the features that render one class of the C...
In this work we propose a statistical approach to handling sources of theoretical uncertainty in str...
The problem of causal inference is to determine if a given probability distribution on observed vari...
Inflation is a Python package that implements inflation algorithms for causal inference. In causal i...
A causal structure is a description of the functional dependencies between random variables. A distr...
We introduce Inflation, a Python library for assessing whether an observed probability distribution ...
Inferring the causal structure that links n observables is usually based upon detecting statistical ...
Different resampling methods for the null hypothesis of no Granger causality are assessed in the set...
yz Causal discovery, for the most part, is concerned with learning causal models in the form of dire...
When performing causal discovery, assumptions have to be made on how the true causal mechanism corre...
Different resampling methods for the null hypothesis of no Granger causality are assessed in the set...
Scientists aim to design experiments and analyze evidence to obtain maximum knowledge. Although scie...
Over the past twenty-five years, a large number of algorithms have been developed to learn the struc...
The current paper develops a probabilistic theory of causation using measure-theoretical concepts an...
Estimating causal relations between two or more variables is an important topic in psychology. Estab...
The aim of this paper is to identify and to characterize the features that render one class of the C...
In this work we propose a statistical approach to handling sources of theoretical uncertainty in str...