FUNDAMENTALS OF STATISTICAL CAUSALITY

By A. P. Dawid. 2007

46 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A Causal Bayesian Networks Viewpoint on Fairness (2018, neutral): “Before doing that, we give some background on CBNs [18,39,40,42,45], assuming that all variables except A are continuous.”
Path-Specific Counterfactual Fairness (2018, methodology): “…& Sh-pitser, 2018; Russell et al., 2017; Zhang et al., 2017; Zhang & Wu, 2017; Zhang & Bareinboim, 2018) that using a causal framework (Pearl, 2000; Dawid, 2007; Pearl et al., 2016; Peters et al., 2017) would lead to a more intuitive, powerful, and less error-prone way of reasoning about fairness.”
Causal Reasoning from Meta-reinforcement Learning (2019, methodology): “There is a rich literature on formal approaches for defining and performing causal reasoning (Pearl, 2000; Spirtes et al., 2000; Dawid, 2007; Pearl et al., 2016).”
Causal reasoning in a prediction task with hidden causes (2015, methodology): “The theory uses a popular probabilistic framework called causal Bayes nets that permits a precise mathematical formulation of causal reasoning under this distinction (Spirtes and Scheines, 2001; Pearl, 2009; Dawid, 2007).”
Statistical Causality from a Decision-Theoretic Perspective (2014, background): “The lecture notes Dawid (2007b) contain a fuller exposition of the decision-theoretic approach.”
A Unified Framework for Resource-Bounded Autonomous Agents Interacting with Unknown Environments (2011, background): “This idea got later independently developed by De Finetti (1937). In his formulation, he proposed a thought experiment where a decision maker is required to set the fee for a gamble involving the likelihood of an event.”
Fairness in Machine Learning (2020, methodology): “We do that by using Causal Bayesian Networks (CBNs) [39, 145, 146, 151, 166], which currently represent the simplest and most intuitive tool for describing relationships among random variables, and therefore different possible unfairness scenarios underlying a dataset.”