55 citations · 85 across the 3 of their papers we have counts for
4 papers
Semi-Instrumental Variables: A Test for Instrument Admissibility
Tianjiao Chu, Richard Scheines, Peter L. Spirtes
In a causal graphical model, an instrument for a variable X and its effect Y is a random variable that is a cause of X and independent of all the causes of Y except X. (Pearl (1995…
Learning Measurement Models for Unobserved Variables
Ricardo Silva, Richard Scheines, Clark Glymour +1
Observed associations in a database may be due in whole or part to variations in unrecorded (latent) variables. Identifying such variables and their causal relationships with one a…
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables
Frederick Eberhardt, Clark Glymour, Richard Scheines
We show that if any number of variables are allowed to be simultaneously and independently randomized in any one experiment, log2(N) + 1 experiments are sufficient and in the worst…
Causal discovery of linear acyclic models with arbitrary distributions
Patrik O. Hoyer, Aapo Hyvarinen, Richard Scheines +4
An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used t…