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Marvin N. Wright

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4 papers

2 author rows

AAAI Conference 2025 Conference Paper

Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests

Kristin Blesch
Niklas Koenen
Jan Kapar
Pegah Golchian
Lukas Burk
Markus Loecher
Marvin N. Wright

This paper proposes a method for measuring conditional feature importance via generative modeling. In explainable artificial intelligence (XAI), conditional feature importance assesses the impact of a feature on a prediction model's performance given the information of other features. Model-agnostic post hoc methods to do so typically evaluate changes in the predictive performance under on-manifold feature value manipulations. Such procedures require creating feature values that respect conditional feature distributions, which can be challenging in practice. Recent advancements in generative modeling can facilitate this. For tabular data, which may consist of both categorical and continuous features, the adversarial random forest (ARF) stands out as a generative model that can generate on-manifold data points without requiring intensive tuning efforts or computational resources, making it a promising candidate model for subroutines in XAI methods. This paper proposes cARFi (conditional ARF feature importance), a method for measuring conditional feature importance through feature values sampled from ARF-estimated conditional distributions. cARFi requires only little tuning to yield robust importance scores that can flexibly adapt for conditional or marginal notions of feature importance, including straightforward extensions to condition on feature subsets and allows for inferring the significance of feature importances through statistical tests.

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ICML Conference 2025 Conference Paper

Fast Estimation of Partial Dependence Functions using Trees

Jinyang Liu
Tessa Steensgaard
Marvin N. Wright
Niklas Pfister
Munir Hiabu

Many existing interpretation methods are based on Partial Dependence (PD) functions that, for a pre-trained machine learning model, capture how a subset of the features affects the predictions by averaging over the remaining features. Notable methods include Shapley additive explanations (SHAP) which computes feature contributions based on a game theoretical interpretation and PD plots (i. e. , 1-dim PD functions) that capture average marginal main effects. Recent work has connected these approaches using a functional decomposition and argues that SHAP values can be misleading since they merge main and interaction effects into a single local effect. However, a major advantage of SHAP compared to other PD-based interpretations has been the availability of fast estimation techniques, such as TreeSHAP. In this paper, we propose a new tree-based estimator, FastPD, which efficiently estimates arbitrary PD functions. We show that FastPD consistently estimates the desired population quantity – in contrast to path-dependent TreeSHAP which is inconsistent when features are correlated. For moderately deep trees, FastPD improves the complexity of existing methods from quadratic to linear in the number of observations. By estimating PD functions for arbitrary feature subsets, FastPD can be used to extract PD-based interpretations such as SHAP, PD plots and higher-order interaction effects.

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ICML Conference 2025 Conference Paper

Gradient-based Explanations for Deep Learning Survival Models

Sophie Hanna Langbein
Niklas Koenen
Marvin N. Wright

Deep learning survival models often outperform classical methods in time-to-event predictions, particularly in personalized medicine, but their "black box" nature hinders broader adoption. We propose a framework for gradient-based explanation methods tailored to survival neural networks, extending their use beyond regression and classification. We analyze the implications of their theoretical assumptions for time-dependent explanations in the survival setting and propose effective visualizations incorporating the temporal dimension. Experiments on synthetic data show that gradient-based methods capture the magnitude and direction of local and global feature effects, including time dependencies. We introduce GradSHAP(t), a gradient-based counterpart to SurvSHAP(t), which outperforms SurvSHAP(t) and SurvLIME in a computational speed vs. accuracy trade-off. Finally, we apply these methods to medical data with multi-modal inputs, revealing relevant tabular features and visual patterns, as well as their temporal dynamics.

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JMLR Journal 2024 Journal Article

Decomposing Global Feature Effects Based on Feature Interactions

Julia Herbinger
Marvin N. Wright
Thomas Nagler
Bernd Bischl
Giuseppe Casalicchio

Global feature effect methods, such as partial dependence plots, provide an intelligible visualization of the expected marginal feature effect. However, such global feature effect methods can be misleading, as they do not represent local feature effects of single observations well when feature interactions are present. We formally introduce generalized additive decomposition of global effects (GADGET), which is a new framework based on recursive partitioning to find interpretable regions in the feature space such that the interaction-related heterogeneity of local feature effects is minimized. We provide a mathematical foundation of the framework and show that it is applicable to the most popular methods to visualize marginal feature effects, namely partial dependence, accumulated local effects, and Shapley additive explanations (SHAP) dependence. Furthermore, we introduce and validate a new permutation-based interaction detection procedure that is applicable to any feature effect method that fits into our proposed framework. We empirically evaluate the theoretical characteristics of the proposed methods based on various feature effect methods in different experimental settings. Moreover, we apply our introduced methodology to three real-world examples to showcase their usefulness. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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