  Motivated with recent advances in inferring users' mental state in social
media posts, we identify and formulate the problem of finding causal indicators
behind mental illness in self-reported text. In the past, we witness the
presence of rule-based studies for causal explanation analysis on curated
Facebook data. The investigation on transformer-based model for multi-class
causal categorization in Reddit posts point to a problem of using long-text
which contains as many as 4000 words. Developing end-to-end transformer-based
models subject to the limitation of maximum-length in a given instance. To
handle this problem, we use Longformer and deploy its encoding on
transformer-based classifier. The experimental results show that Longformer
achieves new state-of-the-art results on M-CAMS, a publicly available dataset
with 62\% F1-score. Cause-specific analysis and ablation study prove the
effectiveness of Longformer. We believe our work facilitates causal analysis of
depression and suicide risk on social media data, and shows potential for
application on other mental health conditions.
