  With recent developments in Social Computing, Natural Language Processing and
Clinical Psychology, the social NLP research community addresses the challenge
of automation in mental illness on social media. A recent extension to the
problem of multi-class classification of mental health issues is to identify
the cause behind the user's intention. However, multi-class causal
categorization for mental health issues on social media has a major challenge
of wrong prediction due to the overlapping problem of causal explanations.
There are two possible mitigation techniques to solve this problem: (i)
Inconsistency among causal explanations/ inappropriate human-annotated
inferences in the dataset, (ii) in-depth analysis of arguments and stances in
self-reported text using discourse analysis. In this research work, we
hypothesise that if there exists the inconsistency among F1 scores of different
classes, there must be inconsistency among corresponding causal explanations as
well. In this task, we fine tune the classifiers and find explanations for
multi-class causal categorization of mental illness on social media with LIME
and Integrated Gradient (IG) methods. We test our methods with CAMS dataset and
validate with annotated interpretations. A key contribution of this research
work is to find the reason behind inconsistency in accuracy of multi-class
causal categorization. The effectiveness of our methods is evident with the
results obtained having category-wise average scores of $81.29 \%$ and $0.906$
using cosine similarity and word mover's distance, respectively.
