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Found 6 papers, 4 papers with code
Counterfactual Learning with Multioutput Deep Kernels
In this paper, we address the challenge of performing counterfactual inference with observational data via Bayesian nonparametric regression adjustment, with a focus on high-dimensional settings featuring multiple actions and multiple correlated outcomes.
Interpretable Deep Causal Learning for Moderation Effects
In this extended abstract paper, we address the problem of interpretability and targeted regularization in causal machine learning models.
Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation
This paper develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Trees and is specifically designed to estimate heterogeneous treatment effects using observational data.
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review
Large observational data are increasingly available in disciplines such as health, economic and social sciences, where researchers are interested in causal questions rather than prediction.
Modelling Grocery Retail Topic Distributions: Evaluation, Interpretability and Stability
Our approach is an alternative to standard label-switching techniques and provides a single posterior summary set of topics, as well as associated measures of uncertainty.
BPEC: An R Package for Bayesian Phylogeographic and Ecological Clustering
BPEC is an R package for Bayesian Phylogeographic and Ecological Clustering which allows geographical, environmental and phenotypic measurements to be combined with DNA sequences in order to reveal clustered structure resulting from migration events.
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