Causality: Models, Reasoning and Inference, Second Edition by Judea Pearl

By Jayanta Kumar Ghosh. 2011

188 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Distilling Causal Effect of Data in Class-Incremental Learning (2021, methodology): “To systematically explain the (anti-)forgetting in CIL in terms of the causal effect of the old knowledge, we frame the data, feature, and label in each incremental learning step into causal graphs [29] (Figure 2).”
Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI (2021, background): “Causability is not a synonym for causality in the sense of Judea Pearl [13]; the term causa-bil-ity was introduced in reference to usa-bil-ity.”
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview (2010, background): “An alternative, and entirely equivalent, approach can be based on the ‘d-separation criterion’ (Pearl, 2009; Verma and Pearl, 1990).”
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology (2017, background): “Graphical models (Lauritzen, 1996) combined with probability theory (Kolmogorov, 1950), which can represent a set of random variables and conditional dependencies, form the basis for causality (Pearl, 2009); this is of extreme importance towards reaching explainable AI.”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, background): “structural causal models (SCM) (Pearl, 2009b) and potential outcomes (Rubin, 1974, 2005), which are complementary and theoretically connected (Pearl, 2009b; Richardson and Robins, 2013; Morgan and Winship, 2015).”
The Impact of Entrepreneurship Education in High School on Long-Term Entrepreneurial Performance (2015, background): “Ultimately, the inclusion of variables can only be guided by the causal knowledge of the investigators (Pearl, 2009: 408).”
Differentiable Causal Discovery Under Latent Interventions (2022, methodology): “…constrained optimization methods for ﬁnding a DAG ( directed acyclic graph ); other approaches include constraint-based (Spirtes et al., 1993; Pearl, 2000; Spirtes et al., 2013) and score-based methods (Bouckaert, 1993; Heckerman et al., 1994; Chickering and Heckerman, 1997; Kuipers et al.,…”