Statistical Causality from a Decision-Theoretic Perspective

By A. P. Dawid. 2014

79 citations recorded by the source corpus.

We present an overview of the decision-theoretic framework of statistical causality, which is well suited for formulating and solving problems of determining the effects of applied causes. The approach is described in detail, and it is related to and contrasted with other current formulations, such as structural equation models and potential responses. Topics and applications covered include confounding, the effect of treatment on the treated, instrumental variables, and dynamic treatment strategies.

Subfields: Artificial Intelligence, Sociology and Political Science, Statistics and Probability

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Citations in the corpus

False Positives , False Negatives , and False Analyses : A Rejoinder to “ Machine Bias : There ’ s Software Used Across the Country to Predict Future Criminals . And It ’ s Biased Against Blacks . ” (2017, unknown): “However, as Imrey and Dawid (2015:18)3 note, the debates and “... considerations [of using ARAIs in such set tings] are properly functions of social policy, not statistical inference.” That is, there might be much to debate about how and why we would use valid ARAIs. The issue that is no longer up ”
Causal Analysis of Agent Behavior for AI Safety (2021, methodology): “However, the formal machinery for reasoning about causal manipulations and their relation to statistical evidence is a relatively recent development (Spirtes et al., 2000; Pearl, 2009; Dawid, 2015).”
Bayesian networks and boundedly rational expectations (2016, background): “Indeed, the impetus behind part of the Bayesian-network literature (Pearl (2000)) is to systematize correct reasoning about identification of causal relations (see Heckman and Pinto (2013), Pearl (2013) and Dawid (2014), for recent reflections on this role of graphical probabilistic models).”
Extended Conditional Independence and Applications in Causal Inference (2015, methodology): “For numerous further applications and illustrations see [3, 19, 20, 21, 22, 23, 25, 30, 34, 36, 37].”
Precision Medicine. (2019, methodology): “We formalize the notion of an optimal treatment regime using the language of potential outcomes (Rubin 1978, 2005; Dawid 2015).”