Seeing and Doing: the Concept of Causation
By D. Lindley. 2002
50 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Sociology and Political Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/43a6f14d53c901a6756b81464743dd724c386191
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Nanny Wermuth
- Judea Pearl
- D. R. Cox
- Franco Taroni
- Silvia Bozza
- Elias Bareinboim
- Ilya Shpitser
- Thomas S. Richardson
In Sociology and Political Science
- D. R. Cox
- Judea Pearl
- Nanny Wermuth
- Rex Bryan Kline
- Elias Bareinboim
- Gary King
- Richard A. Nielsen
- Karthika Mohan
In Statistics and Probability
- Nanny Wermuth
- Judea Pearl
- D. R. Cox
- Silvia Bozza
- Franco Taroni
- Tyler J. VanderWeele
- James M. Robins
- Elias Bareinboim
Citations in the corpus
- 3. The Foundations of Causal Inference (2010, background): “…hypothetical interventions in the model, was proposed in Haavelmo (1943), Strotz and Wold (1960), Spirtes et al. (1993), Pearl (1993a, 2000a), and Lindley (2002), and it has led to new ways of defining and estimating causal effects in nonlinear and nonparametric models (that is, models in which…”
- Causality: a Statistical View (2004, background): “Lindley (2002) has reviewed Pearl’s book and given a lucid account of some essential ideas and made important comments.”
- Comment: Understanding Simpson’s Paradox (2014, background): “Modern analysts explain away Simpson’s paradox by distinguishing seeing from doing (Lindley, 2002).”
- Dismissal of the illusion of uncertainty in the assessment of a likelihood ratio (2016, background): “Related to this is a shortcut widely observed among forensic scientists that equates beliefs with relative frequencies (see Lindley (2002) for discussion on this observation in other contexts).”
- Causal inference in statistics: An overview (2009, methodology): “” This reluctance however is diminishing among Bayesians primarily due to recognition that, orthogonal to the traditional debate between frequentists and subjectivists, causal analysis is about change, and change demands a new vocabulary that distinguishes “seeing” from “doing” (Lindley, 2002) (see ”