Causal Effect Identification by Adjustment under Confounding and Selection Biases

By Juan David Correa, Elias Bareinboim. 2017

46 citations recorded by the source corpus.

Controlling for selection and confounding biases are two of the most challenging problems in the empirical sciences as well as in artificial intelligence tasks. Covariate adjustment (or, Backdoor Adjustment) is the most pervasive technique used for controlling confounding bias, but the same is oblivious to issues of sampling selection. In this paper, we introduce a generalized version of covariate adjustment that simultaneously controls for both confounding and selection biases. We first derive a sufficient and necessary condition for recovering causal effects using covariate adjustment from an observational distribution collected under preferential selection. We then relax this setting to consider cases when additional, unbiased measurements over a set of covariates are available for use (e.g., the age and gender distribution obtained from census data). Finally, we present a complete algorithm with polynomial delay to find all sets of admissible covariates for adjustment when confounding and selection biases are simultaneously present and unbiased data is available.

Subfields: Statistics and Probability

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Citations in the corpus

Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms (2018, background): “Correa and Bareinboim (2017) determine conditions under which interventional distributions are identiﬁed without using external data.”
Adjustment Criteria for Generalizing Experimental Findings (2019, methodology): “Although usually used to control for confounding bias in observational data, it has recently been shown to be suitable to control for when selection bias is present as well (Correa and Bareinboim, 2017; Correa et al., 2018).”
A Review of Generalizability and Transportability (2021, background): “…and Bareinboim, 2014; Pearl, 2015), observational studies (Pearl and Bareinboim, 2011; Pearl, 2015; Bareinboim and Tian, 2015; Bareinboim and Pearl, 2016; Correa and Bareinboim, 2017; Correa, Tian and Bareinboim, 2018), and a combination of heterogeneous studies (Bareinboim and Pearl, 2014, 2016).”
Generalized Adjustment Under Confounding and Selection Biases (2018, background): “Building on these results, Correa and Bareinboim (2017b) devised a set of complete conditions for when none of the covariates are measured (Def.”