CAVS selects, among all valid back-door adjustment sets, the one with the smallest mutual information with the intervention variable, and reports better finite-sample causal effect estimates.
Box plots of errors for CA VS and two baselines (the smallest parents of X that satisfy the back-door criterion, parents of X) on artificial data
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Practically Effective Adjustment Variable Selection in Causal Inference
CAVS selects, among all valid back-door adjustment sets, the one with the smallest mutual information with the intervention variable, and reports better finite-sample causal effect estimates.