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Found 6 papers, 4 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.
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.
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks.
Distributional Anchor Regression
In a causally inspired perspective on OOD generalization, the test data arise from a specific class of interventions on exogenous random variables of the DGP, called anchors.
Methodology
Deep and interpretable regression models for ordinal outcomes
We present ordinal neural network transformation models (ONTRAMs), which unite DL with classical ordinal regression approaches.
