The paper demonstrates that assuming the quantile partial effect lies in a finite linear span enables causal identifiability from observational data, with applications to bivariate and multivariate causal discovery using basis tests and Fisher information.
Inferring cause and effect in the presence of heteroscedastic noise
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Causal Discovery via Quantile Partial Effect
The paper demonstrates that assuming the quantile partial effect lies in a finite linear span enables causal identifiability from observational data, with applications to bivariate and multivariate causal discovery using basis tests and Fisher information.