Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The relationship beten statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches.  proposes a supervised machine learning approach to infer the existence of a directed causal link beten two variables in multivariate settings with n > 2 variables. The approach relies on the asymmetry of some conditional (in) dependence relations beten the members of the Markov blankets of two variables causally connected. Our results show that supervised learning methods may be successfully used to extract causal information on the basis of asymmetric statistical descriptors also for n gt; 2 variate distributions.
['Gianluca Bontempi', 'Maxime Flauder']
I am writing a research paper on a idea From dependency to causality: a machine learning approach
[{"from": "human", "value": "I am writing a research paper on a idea From dependency to causality: a machine learning approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The relationship beten statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches.  proposes a supervised machine learning approach to infer the existence of a directed causal link beten two variables in multivariate settings with n > 2 variables. The approach relies on the asymmetry of some conditional (in) dependence relations beten the members of the Markov blankets of two variables causally connected. Our results show that supervised learning methods may be successfully used to extract causal information on the basis of asymmetric statistical descriptors also for n gt; 2 variate distributions."}]