A Complete Generalized Adjustment Criterion

By Emilija Perkovi'c, Johannes Textor, Markus Kalisch, Marloes H. Maathuis. 2015

82 citations recorded by the source corpus.

Covariate adjustment is a widely used approach to estimate total causal effects from observational data. Several graphical criteria have been developed in recent years to identify valid covariates for adjustment from graphical causal models. These criteria can handle multiple causes, latent confounding, or partial knowledge of the causal structure; however, their diversity is confusing and some of them are only sufficient, but not necessary. In this paper, we present a criterion that is necessary and sufficient for four different classes of graphical causal models: directed acyclic graphs (DAGs), maximum ancestral graphs (MAGs), completed partially directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our criterion subsumes the existing ones and in this way unifies adjustment set construction for a large set of graph classes.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Adjustment Criteria for Generalizing Experimental Findings (2019, background): “Several criteria have been developed to determine whether a set Z is admissible for adjustment (Shpitser et al., 2010; Perković et al., 2015; 2018), including the celebrated “Backdoor criterion” (Pearl, 1993; 2000; Pearl and Paz, 2013), namely: Definition 3 (Backdoor Criterion).”
Robust causal inference using directed acyclic graphs: the R package 'dagitty'. (2017, methodology): “The R package ‘dagitty’ can identify such cases using a recently published generalized version of the back-door criterion (8,16).”