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Found 12 papers, 6 papers with code
Model-based causal feature selection for general response types
Discovering causal relationships from observational data is a fundamental yet challenging task.
Deep conditional transformation models for survival analysis
An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of electronic health records.
Heterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests
Most importantly, the noncollapsibility issue necessitates the joint estimation of treatment and prognostic effects.
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.
Deep interpretable ensembles
We propose a novel transformation ensemble which aggregates probabilistic predictions with the guarantee to preserve interpretability and yield uniformly better predictions than the ensemble members on average.
Probabilistic Time Series Forecasts with Autoregressive Transformation Models
Probabilistic forecasting of time series is an important matter in many applications and research fields.
Deep and interpretable regression models for ordinal outcomes
We present ordinal neural network transformation models (ONTRAMs), which unite DL with classical ordinal regression approaches.
Deep Conditional Transformation Models
Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings.
Deep transformation models: Tackling complex regression problems with neural network based transformation models
Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks, it is predominantly used to just predict a single number.
Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival
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).
Top-down Transformation Choice
The models used in this analysis ranged from evergreens, such as the normal linear regression model with constant variance, to novel models with extremely flexible conditional distribution functions, such as transformation trees and transformation forests.
Transformation Forests
A more general understanding of regression models as models for conditional distributions allows much broader inference from such models, for example the computation of prediction intervals.
