What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?

2 code implementations • 21 Jun 2022 • Susanne Dandl, Torsten Hothorn, Heidi Seibold, Erik Sverdrup, Stefan Wager, Achim Zeileis

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.

Open Science in Software Engineering

2 code implementations • 13 Apr 2019 • Daniel Méndez Fernández, Daniel Graziotin, Stefan Wagner, Heidi Seibold

Open science describes the movement of making any research artefact available to the public and includes, but is not limited to, open access, open data, and open source.

Software Engineering

Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival

1 code implementation • 5 Feb 2019 • Natalia Korepanova, Heidi Seibold, Verena Steffen, Torsten Hothorn

We investigate the effect of the proportional hazards assumption on prognostic and predictive models of the survival time of patients suffering from amyotrophic lateral sclerosis (ALS).

OpenML: An R Package to Connect to the Machine Learning Platform OpenML

1 code implementation • 5 Jan 2017 • Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl

We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks.

BIG-bench Machine Learning