9 citations · 20 across the 5 of their papers we have counts for
5 papers
TSCI: two stage curvature identification for causal inference with invalid instruments
David Carl, Corinne Emmenegger, Peter Bühlmann +1
TSCI implements treatment effect estimation from observational data under invalid instruments in the R statistical computing environment. Existing instrumental variable approaches…
Confidence and Uncertainty Assessment for Distributional Random Forests
Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann +1
The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm to estimate multivariate conditional distributions. Due to its general estimation procedure,…
Treatment Effect Estimation with Observational Network Data using Machine Learning
Corinne Emmenegger, Meta-Lina Spohn, Timon Elmer +1
Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in s…
Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements
Corinne Emmenegger, Peter Bühlmann
Traditionally, spline or kernel approaches in combination with parametric estimation are used to infer the linear coefficient (fixed effects) in a partially linear mixed-effects mo…
Regularizing Double Machine Learning in Partially Linear Endogenous Models
Corinne Emmenegger, Peter Bühlmann
The linear coefficient in a partially linear model with confounding variables can be estimated using double machine learning (DML). However, this DML estimator has a two-stage leas…