Results 1 
3 of
3
Discovering Cyclic Causal Structure
, 1996
"... This paper is concerned with the problem of making causal inferences from observational data, when the underlying causal structure may involve feedback loops. In particular, making causal inferences under the assumption that the causal system which generated the data is linear and that there are no ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
This paper is concerned with the problem of making causal inferences from observational data, when the underlying causal structure may involve feedback loops. In particular, making causal inferences under the assumption that the causal system which generated the data is linear and that there are no unmeasured common causes (latent variables). Linear causal structures of this type can be represented by nonrecursive linear structural equation models. I present a correct polynomial time (on sparse graphs) discovery algorithm for linear cyclic models that contain no latent variables. This algorithm outputs a representation of a class of nonrecursive linear structural equation models, given observational data as input. Under the assumption that all conditional independencies found in the observational data are true for structural reasons rather than because of particular parameter values, the algorithm discovers causal features of the structure which generated the data. A simple modification of the algorithm can be used)as a decision procedure (whose runtime is polynomial in the number of vertices) for determining when two
Osaka University
"... When observational data is available from practical studies and a directed cyclic graph for how various variables affect each other is known based on substantive understanding of the process, we consider a problem in which a control plan of a treatment variable is conducted in order to bring a res ..."
Abstract
 Add to MetaCart
(Show Context)
When observational data is available from practical studies and a directed cyclic graph for how various variables affect each other is known based on substantive understanding of the process, we consider a problem in which a control plan of a treatment variable is conducted in order to bring a response variable close to a target value with variation reduction. We formulate an optimal control plan concerning a certain treatment variable through path coefficients in the framework of linear nonrecursive structural equation models. Based on the formulation, we clarify the properties of causal effects when conducting a control plan. The results enable us to evaluate the effect of a control plan on the variance from observational data. 1