5 projects
transportable-causal-bootstrapping
Transport formula analysis, transportable causal weights, and bootstrap sampling
causal-sampler
Causal resampling with bootstrap, weighted mixtures, hybrid Gibbs-HMC and ID-guided diffusion.
causalbootstrapping
Causal identification and weighted empirical resampling for causal analysis.
mechanism-learn
Mechanism-learn is a simple method to deconfound observational data such that any appropriate machine learning model is forced to learn predictive relationships between effects and their causes, despite the potential presence of multiple unknown and unmeasured confounding. The library is compatible with most existing ML deployments. The library is compatible with most existing ML deployments such as models built with Scikit-learn and Keras.
PFFRA
An Interpretable Machine Learning technique to analyse the contribution of features in the frequency domain. This method is inspired by permutation feature importance analysis but aims to quantify and analyse the time-series predictive model's mechanism from a global perspective.
