Statistics and causal inference: A review

By Judea Pearl. 2003

180 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/211680a75afaa0a4130a47530af554ea0de75629

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Causal inference in statistics: An overview (2009, background): “Likewise, the standard decision to assume independence among certain statistical parameters and not others (in a Bayesian prior) rely on causal information (see discussions with Joseph Kadane and Serafin Moral (Pearl, 2003)).”
Estimating brain functional connectivity with sparse multivariate autoregression (2005, background): “A different line of work is represented by Pearl (1998, 2000, 2003) and Spirtes et al .”
Causal diagrams for interference (2014, methodology): “nmeasured confoundingassumption.” Identifying the variables that mustbeincluded in Ccan beassessed with theaid of causal diagrams (e.g., Greenland and Robins, 1986; Greenland, Pearl and Robins, 1999; Pearl, 2003). Causal diagrams, or causal directed acyclic graphs (DAGs) consist of nodes, representi”
Effective connectivity: Influence, causality and biophysical modeling (2011, unknown): “058 Despite philosophical disagreements about the study of causality, there seems to be a consensus that causal modeling is a legitimate statistical enterprise (Cox and Wermuth, 2004; Frosini, 2006; Pearl, 2003).”
Estimating high-dimensional intervention effects from observational data (2008, methodology): “There is a large existing literature on estimating the equivalence class of DAGs (e.g., [2, 3, 4, 12, 14, 30, 31] and [33]), and there is also a large literature on estimating causal eﬀects when a DAG is given (e.g., [18, 19, 23, 24] and [25]).”