The Book of Why: The New Science of Cause and Effect†

By Lisa R. Goldberg. 2019

363 citations recorded by the source corpus.

Judea Pearl is on a mission to change the way we interpret data. An eminent professor of computer science, Pearl has documented his research and opinions in scholarly books and papers. Now, he has ...

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Causal Inference & Discovery

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Citations in the corpus

Abductive Commonsense Reasoning (2019, background): “…has long been considered to be at the core of understanding narratives (Hobbs et al., 1988), reading between the lines (Norvig, 1987; Charniak & Shimony, 1990), reasoning about everyday situations (Peirce, 1965b; Andersen, 1973), and counterfactual reasoning (Pearl, 2002; Pearl & Mackenzie, 2018).”
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution (2018, positive): “I call this transformation “The Causal Revolution,” (Pearl and Mackenzie, 2018) and the mathematical framework that led to it I call “Structural Causal Models (SCM).””
Explainability fact sheets: a framework for systematic assessment of explainable approaches (2019, neutral): “Given an actionable , and not causal, explanation, the users should understand that the action suggested by the explanation will result in, say, a diﬀerent classiﬁcation outcome, however interpreting it causally, no matter how tempting, can lead to inaccurate insights [38].”
Bias and Debias in Recommender System: A Survey and Future Directions (2020, neutral): “For more details on causal inference, we refer the readers to the excellent causal textbook [134].”
Foundations of structural causal models with cycles and latent variables (2016, neutral): “This hierarchy allows us to compare SCMs at different levels of abstraction and formally establishes the “ladder” of causation (last two implications) [64, 50, 48].”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, neutral): “Under what circumstances will AI be su ﬃ cient and when will it need to be supported by causal reasoning or simulation (Pearl & MacKenzie, 2018)?”
What if This Modified That? Syntactic Interventions with Counterfactual Embeddings (2021, background): “We borrow the term “counterfactual” from causal literature because z′ represents what zk would have been if zp had been different (Pearl and Mackenzie, 2018).”