Understanding the effects of psychiatric medication during mental health treatment constitutes an active area of inquiry. While clinical trials help evaluate the effects of these medications, many suffer from a lack of generalizability in large populations. We leverage social media data to examine psychopathological effects subject to self-reported usage of psychiatric medication. Using a list of common approved and regulated psychiatric drugs and a Twitter dataset of 300M posts from 30K individuals, we develop machine learning models to assess effects relating to mood, cognition, depression, anxiety, psychosis, and suicidal ideation. Based on a propensity score based causal analysis, we observe that usage of specific drugs are associated with characteristic changes in an individual's psychopathology. We situate these observations in the psychiatry literature, with a deeper analysis of pre-treatment cues that predict treatment outcomes, and post-treatment linguistic markers correlated with positive out- comes. Our work bears potential to inspire novel clinical investigations and to build tools for digital therapeutics.