Causation

By MS Isamu Hayata Phd Surabhi Kakati MITSUO OSHIMURA, Petra Sandberg. 2004

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Toward Causal Representation Learning (2021, positive): “Causal relations can also be viewed as the components of reasoning chains [151] that provide predictions for situations that are very far from the observed distribution and may even remain purely hypothetical [163], [183] or require conscious deliberation [128].”
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows (2017, positive): “Consequently, how to infer causal relations from observational data or combinations of observational and experimental data, known as causal discovery [9], [6], has drawn much attention in several disciplines in the past three decades.”
Ancestral Causal Inference (2016, unknown): “Finally, we assume that the Causal Markov Assumption and the Causal Faithfulness Assumption [23] both hold.”
UvA-DARE ( Digital Academic Repository ) Causal Consistency of Structural Equation Models (2017, unknown): “paper are Structural Equation Models (SEMs, Section 2, Section 3) [16, 19].”
Learning Bayesian networks from data: An information-theory based approach (2002, unknown): “The independence relationships, however, are sufficient to define the essential graph (also called “pattern”, see [49]) of the underlying BN, where the essential graph of a BN is a graph that has the same edges”
Cost overruns in transportation infrastructure projects: Sowing the seeds for a probabilistic theory of causation (2016, unknown): “…has been typically dominated by advocates of a causal mechanical view (e.g., Salmon, 1984, Salmon, 2000), those that take a counterfactual stand, which have been based primarily upon the work of Lewis (1973), and dualists, who seek to combine both the aforementioned perspectives (e.g., Hall, 2004).”
Causation (2005, unknown): “Even more differentiated typologies have been elaborated by philosophers (e.g. Sosa and Tooley, 1993).”
On the Testable Implications of Causal Models with Hidden Variables (2002, unknown): “…a Bayesian network invokes unobserved variables, or hidden variables, the network structure may impose equality and inequality constraints on the distribu tion of the observed variables, and those constraints may not be expressed as conditional independencies [Spirtes et al., 1993, Pearl, 1995].”