In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships. We also study the combination of the proposed measures with standard statistical measures in the framework of the ChaLearn cause-effect pair challenge. The developed model obtains an AUC score of 0.82 on the final test database and ranked second in the challenge.Peer Reviewe
Financial support from the Natural Sciences and Engineering Research Council of Canada and from the...
AbstractA definition for similarity between possibility distributions is introduced and discussed as...
We describe eight data sets that together formed the CauseEffectPairs task in the Causality Challeng...
In this paper we derive variability measures for the conditional probability distributions of a pair...
Detection of a causal relationship between two or more sets of data is an important problem across v...
We propose a new approach to infer the causal structure that has generated the observed statistical ...
International audienceThis chapter addresses the problem of benchmarking causal models or validating...
Instrumental variables have proven useful, in particular within the social sciences and economics, f...
We are interested in learning causal relationships between pairs of random variables, purely from ob...
We propose a nonparametric estimator and a nonparametric test for Granger causality measures that qu...
We propose a nonparametric estimator and a nonparametric test for Granger causality measures that qu...
People’s causal judgments exhibit substantial variability, but the processes that lead to this varia...
The current paper develops a probabilistic theory of causation using measure-theoretical concepts an...
We describe a method that infers whether statistical dependences between two observed variables X an...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
Financial support from the Natural Sciences and Engineering Research Council of Canada and from the...
AbstractA definition for similarity between possibility distributions is introduced and discussed as...
We describe eight data sets that together formed the CauseEffectPairs task in the Causality Challeng...
In this paper we derive variability measures for the conditional probability distributions of a pair...
Detection of a causal relationship between two or more sets of data is an important problem across v...
We propose a new approach to infer the causal structure that has generated the observed statistical ...
International audienceThis chapter addresses the problem of benchmarking causal models or validating...
Instrumental variables have proven useful, in particular within the social sciences and economics, f...
We are interested in learning causal relationships between pairs of random variables, purely from ob...
We propose a nonparametric estimator and a nonparametric test for Granger causality measures that qu...
We propose a nonparametric estimator and a nonparametric test for Granger causality measures that qu...
People’s causal judgments exhibit substantial variability, but the processes that lead to this varia...
The current paper develops a probabilistic theory of causation using measure-theoretical concepts an...
We describe a method that infers whether statistical dependences between two observed variables X an...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
Financial support from the Natural Sciences and Engineering Research Council of Canada and from the...
AbstractA definition for similarity between possibility distributions is introduced and discussed as...
We describe eight data sets that together formed the CauseEffectPairs task in the Causality Challeng...