Discovering causal relations among observed variables in a given data set is a main topic in studies of statistics and artificial intelligence. Recently, some techniques to disco...
We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relatio...
Frederick Eberhardt, Patrik O. Hoyer, Richard Sche...
In distributed IT systems, replication of information is commonly used to strengthen the fault tolerance on a technical level or the autonomy of an organization on a business level...
Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children rec...
David M. Sobel, Joshua B. Tenenbaum, Alison Gopnik
We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause...
Ricardo Silva, Richard Scheines, Clark Glymour, Pe...
Aiming at explicit description of temporal meaning of causal relations generated by qualitative reasoning systems, this article proposes a causal time ontology which defines a se...
We introduce a logical formalism of irreflexivc causal production relations that possesses both a standard monotonic semantics, and a natural nonmonotonic semantics. The formalism...
While recent corpus annotation efforts cover a wide variety of semantic structures, work on temporal and causal relations is still in its early stages. Annotation efforts have typ...
Steven Bethard, William Corvey, Sara Klingenstein,...
The annotation of causal relations in natural language texts can lead to a low inter-annotator agreement. A French corpus annotated with causal relations would be helpful for the ...
Finding temporal and causal relations is crucial to understanding the semantic structure of a text. Since existing corpora provide no parallel temporal and causal annotations, we ...