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Found 4 papers, 3 papers with code
Quantile Causal Discovery
Causal inference using observational data is challenging, especially in the bivariate case.
Structural restrictions in local causal discovery: identifying direct causes of a target variable
In this paper, we discuss different assumptions for the data-generating process of the target variable under which the set of direct causes is identifiable from the distribution.
Analyzing privacy-aware mobility behavior using the evolution of spatio-temporal entropy
We also evaluate the prediction accuracy of a global GAM compared to individual GAMs and individual AutoRegressive Integrated Moving Average (ARIMA) models.
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery
Causal inference using observational data is challenging, especially in the bivariate case.
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