The Do-Calculus Revisited

By Judea Pearl. 2012

196 citations recorded by the source corpus.

The do-calculus was developed in 1995 to facilitate the identification of causal effects in non-parametric models. The completeness proofs of [Huang and Valtorta, 2006] and [Shpitser and Pearl, 2006] and the graphical criteria of [Tian and Shpitser, 2010] have laid this identification problem to rest. Recent explorations unveil the usefulness of the do-calculus in three additional areas: mediation analysis [Pearl, 2012], transportability [Pearl and Bareinboim, 2011] and metasynthesis. Meta-synthesis (freshly coined) is the task of fusing empirical results from several diverse studies, conducted on heterogeneous populations and under different conditions, so as to synthesize an estimate of a causal relation in some target environment, potentially different from those under study. The talk surveys these results with emphasis on the challenges posed by meta-synthesis. For background material, see this http URL

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Citations in the corpus

Counterfactual reasoning and learning systems: the example of computational advertising (2012, background): “Pearl’s do-calculus completely solves the identifiability problem and provides useful tools to address many instances of the transportability problem (see Pearl, 2012).”
Response to Leslie Hayduk’s Review of Principles and Practice of Structural Equation Modeling, 4th Edition (2018, background): “Les is right in that some concepts from the SCM are not covered in the 4th edition, including Pearl’s do-calculus, which has implications for the identification of causal effects in nonparametric models and mediation analysis, among other topics (Pearl 2014).”
Causal Transportability with Limited Experiments (2013, background): “Conclusions that are obtained in a laboratory setting are transported and applied elsewhere, in an environment that differs in many aspects from that of the laboratory [Pearl, 2012].”
Neural Network Attributions: A Causal Perspective (2019, methodology): “) operator (Deﬁnition 4.1)(Pearl, 2009; 2012) is used to identify causal effects from a given SCM or causal Bayesian network.”
Anti-discrimination learning: a causal modeling-based framework (2017, background): “The complete criterion for the identiﬁability of the post-intervention distribution is given by the do -calculus [25].”