  Research community has witnessed substantial growth in the detection of
mental health issues and their associated reasons from analysis of social
media. We introduce a new dataset for Causal Analysis of Mental health issues
in Social media posts (CAMS). Our contributions for causal analysis are
two-fold: causal interpretation and causal categorization. We introduce an
annotation schema for this task of causal analysis. We demonstrate the efficacy
of our schema on two different datasets: (i) crawling and annotating 3155
Reddit posts and (ii) re-annotating the publicly available SDCNL dataset of
1896 instances for interpretable causal analysis. We further combine these into
the CAMS dataset and make this resource publicly available along with
associated source code: https://github.com/drmuskangarg/CAMS. We present
experimental results of models learned from CAMS dataset and demonstrate that a
classic Logistic Regression model outperforms the next best (CNN-LSTM) model by
4.9\% accuracy.
