Add to ORKGWe propose a method for inferring the existence of a latent common cause ("confounder") of two observed random variables. The method assumes that the two effects of the confounder are (possibly nonlinear) functions of the confounder plus independent, additive noise. We discuss under which conditions the model is identifiable (up to an arbitrary reparameterization of the confounder) from the joint distribution of the effects. We state and prove a theoretical result that provides evidence for the conjecture that the model is generically identifiable under suitable technical conditions. In addition, we propose a practical method to estimate the confounder from a finite i.i.d. sample of the effects and illustrate that the method works well on b...
Causal inference in observational studies can be challenging when confounders are subject to missing...
Inferring the causal structure of a set of random variables from a finite sample of the joint distri...
Inferring the causal structure of a set of random variables from a finite sample of the joint distri...
We propose a method for inferring the existence of a latent common cause ("confounder") of two obser...
We propose a method to classify the causal relationship between two discrete variables given only th...
We propose a method to classify the causal relationship between two discrete variables given only th...
We propose a method to classify the causal relationship between two discrete variables given only th...
Knowledge about causal relationships is important because it enables the prediction of the effects o...
Knowledge about causal relationships is important because it enables the prediction of the effects o...
Given a set of experiments in which varying subsets of observed variables are subject to interventio...
Given a set of experiments in which varying subsets of observed variables are subject to interventio...
Given a set of experiments in which varying subsets of observed variables are subject to interventio...
A recent method for causal discovery is in many cases able to infer whether X causes Y or Y causes X...
A recent method for causal discovery is in many cases able to infer whether X causes Y or Y causes X...
We consider two variables that are related to each other by an invertible function. While it has pre...
Causal inference in observational studies can be challenging when confounders are subject to missing...
Inferring the causal structure of a set of random variables from a finite sample of the joint distri...
Inferring the causal structure of a set of random variables from a finite sample of the joint distri...
We propose a method for inferring the existence of a latent common cause ("confounder") of two obser...
We propose a method to classify the causal relationship between two discrete variables given only th...
We propose a method to classify the causal relationship between two discrete variables given only th...
We propose a method to classify the causal relationship between two discrete variables given only th...
Knowledge about causal relationships is important because it enables the prediction of the effects o...
Knowledge about causal relationships is important because it enables the prediction of the effects o...
Given a set of experiments in which varying subsets of observed variables are subject to interventio...
Given a set of experiments in which varying subsets of observed variables are subject to interventio...
Given a set of experiments in which varying subsets of observed variables are subject to interventio...
A recent method for causal discovery is in many cases able to infer whether X causes Y or Y causes X...
A recent method for causal discovery is in many cases able to infer whether X causes Y or Y causes X...
We consider two variables that are related to each other by an invertible function. While it has pre...
Causal inference in observational studies can be challenging when confounders are subject to missing...
Inferring the causal structure of a set of random variables from a finite sample of the joint distri...
Inferring the causal structure of a set of random variables from a finite sample of the joint distri...