Generalized Adjustment Under Confounding and Selection Biases

By Juan David Correa, Jin Tian, Elias Bareinboim. 2018

60 citations recorded by the source corpus.

Selection and confounding biases are the two most common impediments to the applicability of causal inference methods in large-scale settings. We generalize the notion of backdoor adjustment to account for both biases and leverage external data that may be available without selection bias (e.g., data from census). We introduce the notion of adjustment pair and present complete graphical conditions for identifying causal effects by adjustment. We further design an algorithm for listing all admissible adjustment pairs in polynomial delay, which is useful for researchers interested in evaluating certain properties of some admissible pairs but not all (common properties include cost, variance, and feasibility to measure). Finally, we describe a statistical estimation procedure that can be performed once a set is known to be admissible, which entails different challenges in terms of finite samples.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Graphical criteria for efficient total effect estimation via adjustment in causal linear models (2019, background): “Correa et al. (2018) give a necessary and sufficient graphical criterion for causal effect estimation under confounding and selection bias.”
Adjustment Criteria for Generalizing Experimental Findings (2019, methodology): “, 2014), and (Correa et al., 2018) for listing separating sets in a graph.”