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Found 12 papers, 7 papers with code
A Guide to Feature Importance Methods for Scientific Inference
Understanding the DGP requires insights into feature-target associations, which many ML models cannot directly provide, due to their opaque internal mechanisms.
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome.
Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research
Despite progress in the field, significant parts of current XAI research are still not on solid conceptual, ethical, or methodological grounds.
Efficient SAGE Estimation via Causal Structure Learning
We propose $d$-SAGE, a method that accelerates SAGE approximation.
Improvement-Focused Causal Recourse (ICR)
We demonstrate that given correct causal knowledge, ICR, in contrast to existing approaches, guides towards both acceptance and improvement.
Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena
These descriptors are IML methods that provide insight not just into the model, but also into the properties of the phenomenon the model is designed to represent.
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions.
A Causal Perspective on Meaningful and Robust Algorithmic Recourse
Thus, an action that changes the prediction in the desired way may not lead to an improvement of the underlying target.
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)
Direct importance provides causal insight into the model's mechanism, yet it fails to expose the leakage of information from associated but not directly used variables.
Relative Feature Importance
Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model.
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and Shapley values provide insightful model interpretations, but can lead to wrong conclusions if applied incorrectly.
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach
In addition, we apply the conditional subgroups approach to partial dependence plots (PDP), a popular method for describing feature effects that can also suffer from extrapolation when features are dependent and interactions are present in the model.
