189 citations · 846 across the 31 of their papers we have counts for
9 papers · 1 filter
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…
Strong Faithfulness and Uniform Consistency in Causal Inference
Jiji Zhang, Peter L. Spirtes
A fundamental question in causal inference is whether it is possible to reliably infer manipulation effects from observational data. There are a variety of senses of asymptotic rel…
A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables
Jiji Zhang, Peter L. Spirtes
Different directed acyclic graphs (DAGs) may be Markov equivalent in the sense that they entail the same conditional independence relations among the observed variables. Chickering…
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
Ayesha R. Ali, Thomas S. Richardson, Peter L. Spirtes +1
It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explana…
A theoretical study of Y structures for causal discovery
Subramani Mani, Peter L. Spirtes, Gregory F. Cooper
There are several existing algorithms that under appropriate assumptions can reliably identify a subset of the underlying causal relationships from observational data. This paper i…
Adjacency-Faithfulness and Conservative Causal Inference
Joseph Ramsey, Jiji Zhang, Peter L. Spirtes
Most causal inference algorithms in the literature (e.g., Pearl (2000), Spirtes et al. (2000), Heckerman et al. (1999)) exploit an assumption usually referred to as the causal Fait…