Causal Generative Explainers using Counterfactual Inference: A Case
  Study on the Morpho-MNIST Dataset
Will Taylor-Melanson Zahra Sadeghi Stan Matwin
In this paper, we propose leveraging causal generative learning as an
interpretable tool for explaining image classifiers. Specifically, we present a
generative counterfactual inference approach to study the influence of visual
features (i.e., pixels) as well as causal factors through generative learning.
To this end, we first uncover the most influential pixels on a classifier's
decision by varying the value of a causal attribute via counterfactual
inference and computing both Shapely and contrastive explanations for
counterfactual images with these different attribute values. We then establish
a Monte-Carlo mechanism using the generator of a causal generative model in
order to adapt Shapley explainers to produce feature importances for the
human-interpretable attributes of a causal dataset in the case where a
classifier has been trained exclusively on the images of the dataset. Finally,
we present optimization methods for creating counterfactual explanations of
classifiers by means of counterfactual inference, proposing straightforward
approaches for both differentiable and arbitrary classifiers. We exploit the
Morpho-MNIST causal dataset as a case study for exploring our proposed methods
for generating counterfacutl explantions. We employ visual explanation methods
from OmnixAI open source toolkit to compare them with our proposed methods. By
employing quantitative metrics to measure the interpretability of
counterfactual explanations, we find that our proposed methods of
counterfactual explanation offer more interpretable explanations compared to
those generated from OmnixAI. This finding suggests that our methods are
well-suited for generating highly interpretable counterfactual explanations on
causal datasets.