Search Results for author:
Found 4 papers, 3 papers with code
Proximal Causal Learning of Conditional Average Treatment Effects
Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators currently available.
What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
A related approach, called "model-based forests", that is geared towards randomized trials and simultaneously captures effects of both prognostic and predictive variables, was introduced by Seibold, Zeileis and Hothorn (2018) along with a modular implementation in the R package model4you.
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation.
Doubly robust treatment effect estimation with missing attributes
We find, however, that doubly robust modifications of standard methods for average treatment effect estimation with missing data repeatedly perform better than their non-doubly robust baselines; for example, doubly robust generalized propensity score methods beat inverse-weighting with the generalized propensity score.
Methodology 93C41, 62G35, 62F35, 62P10
