Explaining Text Classifiers with Counterfactual Representations
Pirmin Lemberger Antoine Saillenfest
One well motivated explanation method for classifiers leverages
counterfactuals which are hypothetical events identical to real observations in
all aspects except for one categorical feature. Constructing such
counterfactual poses specific challenges for texts, however, as some attribute
values may not necessarily align with plausible real-world events. In this
paper we propose a simple method for generating counterfactuals by intervening
in the space of text representations which bypasses this limitation. We argue
that our interventions are minimally disruptive and that they are theoretically
sound as they align with counterfactuals as defined in Pearl's causal inference
framework. To validate our method, we first conduct experiments on a synthetic
dataset of counterfactuals, allowing for a direct comparison between classifier
predictions based on ground truth counterfactuals (obtained through explicit
text interventions) and our counterfactuals, derived through interventions in
the representation space. Second, we study a real world scenario where our
counterfactuals can be leveraged both for explaining a classifier and for bias
mitigation.