A sound and complete local learning procedure that identifies valid adjustment sets for nonparametric average causal effect estimation inside a characterized boundary, without pretreatment or causal sufficiency assumptions.
Using R1, the variable S = temp4 satisfies the conditional independence tests: temp4 ̸ ⊥ ⊥udbytte | {lai4}, f oto 4 ⊥ ⊥udbytte | {lai4, temp4}
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Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
A sound and complete local learning procedure that identifies valid adjustment sets for nonparametric average causal effect estimation inside a characterized boundary, without pretreatment or causal sufficiency assumptions.