Adapting to Change: Robust Counterfactual Explanations in Dynamic Data
  Landscapes
Bardh Prenkaj Mario Villaizan-Vallelado Tobias Leemann Gjergji Kasneci
We introduce a novel semi-supervised Graph Counterfactual Explainer (GCE)
methodology, Dynamic GRAph Counterfactual Explainer (DyGRACE). It leverages
initial knowledge about the data distribution to search for valid
counterfactuals while avoiding using information from potentially outdated
decision functions in subsequent time steps. Employing two graph autoencoders
(GAEs), DyGRACE learns the representation of each class in a binary
classification scenario. The GAEs minimise the reconstruction error between the
original graph and its learned representation during training. The method
involves (i) optimising a parametric density function (implemented as a
logistic regression function) to identify counterfactuals by maximising the
factual autoencoder's reconstruction error, (ii) minimising the counterfactual
autoencoder's error, and (iii) maximising the similarity between the factual
and counterfactual graphs. This semi-supervised approach is independent of an
underlying black-box oracle. A logistic regression model is trained on a set of
graph pairs to learn weights that aid in finding counterfactuals. At inference,
for each unseen graph, the logistic regressor identifies the best
counterfactual candidate using these learned weights, while the GAEs can be
iteratively updated to represent the continual adaptation of the learned graph
representation over iterations. DyGRACE is quite effective and can act as a
drift detector, identifying distributional drift based on differences in
reconstruction errors between iterations. It avoids reliance on the oracle's
predictions in successive iterations, thereby increasing the efficiency of
counterfactual discovery. DyGRACE, with its capacity for contrastive learning
and drift detection, will offer new avenues for semi-supervised learning and
explanation generation.