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Found 2 papers, 2 papers with code
Adjustment Identification Distance: A gadjid for Causal Structure Learning
Evaluating graphs learned by causal discovery algorithms is difficult: The number of edges that differ between two graphs does not reflect how the graphs differ with respect to the identifying formulas they suggest for causal effects.
Exploiting Independent Instruments: Identification and Distribution Generalization
Most of the existing estimators assume that the error term in the response $Y$ and the hidden confounders are uncorrelated with the instruments $Z$.
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