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Found 7 papers, 5 papers with code
Learning sources of variability from high-dimensional observational studies
Causal inference studies whether the presence of a variable influences an observed outcome.
Simplest Streaming Trees
In a benchmark suite containing 72 classification problems (the OpenML-CC18 data suite), we illustrate that our approach, Stream Decision Forest (SDF), does not suffer from either of the aforementioned limitations.
When are Deep Networks really better than Decision Forests at small sample sizes, and how?
Empirically, we compare these two strategies on hundreds of tabular data settings, as well as several vision and auditory settings.
The Chi-Square Test of Distance Correlation
One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data.
High-dimensional and universally consistent k-sample tests
The evaluation included several popular independence statistics and covered a comprehensive set of simulations.
hyppo: A Multivariate Hypothesis Testing Python Package
We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing.
Learning Interpretable Characteristic Kernels via Decision Forests
Decision forests are widely used for classification and regression tasks.
