3 citations · 3 across the 5 of their papers we have counts for
5 papers
path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
Claudio Meggio, Johan Pensar, Riccardo De Bin
We present path_boost, a Python package for interpretable supervised learning on graph-structured input data. The package implements PathBoost, a gradient boosting algorithm that a…
tmQM-RDF Dataset: a Knowledge Graph Representing Transition Metal Complexes
Luca Cibinel, Trond Linjordet, Johan Pensar +3
Transition Metal Complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs a…
Causal inference amid missingness-specific independencies and mechanism shifts
Johan de Aguas, Leonard Henckel, Johan Pensar +1
The recovery of causal effects in structural models with missing data often relies on -graphs, which assume that missingness mechanisms do not directly influence substantive var…
Bayesian estimation of causal effects from observational categorical data
Vera Kvisgaard, Johan Pensar
We present a Bayesian procedure for estimation of pairwise intervention effects in a high-dimensional system of categorical variables. We assume that we have observational data gen…
Recovery and inference of causal effects with sequential adjustment for confounding and attrition
Johan de Aguas, Johan Pensar, Tomás Varnet Pérez +1
Confounding bias and selection bias bring two significant challenges to the validity of conclusions drawn from applied causal inference. The latter can stem from informative missin…