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Found 9 papers, 5 papers with code
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.
Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation
Surrogate models play a crucial role in retrospectively interpreting complex and powerful black box machine learning models via model distillation.
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
A decision can be defined as fair if equal individuals are treated equally and unequals unequally.
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations
This work introduces interpretable regional descriptors, or IRDs, for local, model-agnostic interpretations.
counterfactuals: An R Package for Counterfactual Explanation Methods
Counterfactual explanation methods provide information on how feature values of individual observations must be changed to obtain a desired prediction.
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.
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.
Multi-Objective Counterfactual Explanations
We show the usefulness of MOC in concrete cases and compare our approach with state-of-the-art methods for counterfactual explanations.
