TRYGVE HAAVELMO AND THE EMERGENCE OF CAUSAL CALCULUS

By Judea Pearl. 2014

70 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution (2018, background): “For example, how likely it is that Joe’s swimming exercise was a necessary (or sufficient) cause of Joe’s death (Pearl, 2015a; Halpern and Pearl, 2005).”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, methodology): “The papers by Heckman and Pinto (2015) and Pearl (2015) explicitly use a counterfactual setting of the type we use here.”
The seven tools of causal inference, with reflections on machine learning (2019, background): “For example, how likely it is that Joe’s swimming exercise was a necessary (or sufficient) cause of Joe’s death [Halpern and Pearl 2005; Pearl 2015a].”
Single World Intervention Graphs ( SWIGs ) : A Unification of the Counterfactual and Graphical Approaches to Causality (2013, background): “This leads us to question whether NPSEM-IEs and potential outcomes are functionally ‘equivalent’ theories as is claimed by (Pearl, 2000, 2009, 2012a).”
The Book of Why: The New Science of Cause and Effect† (2019, background): “…between Pearl and Rubin disciples (Rubin himself does not seem to participate – in that forum, at least) or to the trib-utes written by Pearl (2015) and Heckman and Pinto (2015) to the reclusive Nobel Laureate, Trygve Haavelmo, who pioneered causal inference in economics in the 1940s…”
Causal inference (2011, background): “Statisticians and econometricians (see Haavelmo [9], Wermuth [35] and Pearl [28] for discussion) have taken the view that the coeffi cients of structural models have no clear meaning because they are not connected to the probability distribution of internal variables.”