Add to ORKGWe propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (ngt;=4) binary variables X_1,...,X_n. Our inference principle states that the factorization of the joint probability into conditional probabilities for X_j given X_1,...,X_j-1 often leads to simpler terms if the order of variables is compatible with the directed acyclic graph representing the causal structure. We study joint measures of OR/AND gates and show that the complexity of the conditional probabilities (the so-called Markov kernels), defined by a hierarchy of exponential models, depends on the order of the variables. Some toy and real-data experiments support our inference rule
One of the common obstacles for learning causal models from data is that high-order conditional inde...
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A...
The causal Markov condition (CMC) is a postulate that links observations to causality. It describes ...
We propose a new algorithm for estimating the causal structure that underlies the observed dependenc...
We propose a new approach to infer the causal structure that has generated the observed statistical ...
We propose a new inference rule for estimating causal structure that underlies the observed statisti...
Inferring the causal structure that links $n$ observables is usually based upon detecting statistic...
We propose a new inference rule for estimating causal structure that underlies the observed statist...
We propose a method to quantify the complexity of conditional probability measures by a Hilbert spac...
Over the past twenty-five years, a large number of algorithms have been developed to learn the struc...
Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure le...
AbstractModels of complex phenomena often consist of hypothetical entities called “hidden causes,” w...
We propose a method to evaluate the complexity of probability measures from data that is based on a ...
We propose a method to evaluate the complexity of probability measures from data that is based on a ...
AbstractThe Markov condition describes the conditional independence relations present in a causal mo...
One of the common obstacles for learning causal models from data is that high-order conditional inde...
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A...
The causal Markov condition (CMC) is a postulate that links observations to causality. It describes ...
We propose a new algorithm for estimating the causal structure that underlies the observed dependenc...
We propose a new approach to infer the causal structure that has generated the observed statistical ...
We propose a new inference rule for estimating causal structure that underlies the observed statisti...
Inferring the causal structure that links $n$ observables is usually based upon detecting statistic...
We propose a new inference rule for estimating causal structure that underlies the observed statist...
We propose a method to quantify the complexity of conditional probability measures by a Hilbert spac...
Over the past twenty-five years, a large number of algorithms have been developed to learn the struc...
Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure le...
AbstractModels of complex phenomena often consist of hypothetical entities called “hidden causes,” w...
We propose a method to evaluate the complexity of probability measures from data that is based on a ...
We propose a method to evaluate the complexity of probability measures from data that is based on a ...
AbstractThe Markov condition describes the conditional independence relations present in a causal mo...
One of the common obstacles for learning causal models from data is that high-order conditional inde...
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A...
The causal Markov condition (CMC) is a postulate that links observations to causality. It describes ...