In recent years there have been significant advances in the use of psychedelic drugs (e.g., psilocybin) to treat depression, anxiety, addiction, and post-traumatic stress disorder. Interest in novel treatments for mental health conditions is fueled by the high added costs of these conditions, including direct medical costs and indirect costs such as productivity losses, as well as the relatively high “failure” rates of most standard treatments. In this presentation we discuss the main challenges faced when developing economic evaluations of psychedelics in the treatment of depression. These challenges can be summarized as follows: (1) outcomes data from randomized controlled trials are scant, partly due to past legal restrictions on medical research on psychedelics, but also due to the challenges of blinding treatments and creating control groups; (2) there are numerous scales used to measure outcomes, all of which are associated with tradeoffs; (3) there is a lack of data on longer-term outcomes, which are particularly important in mental health conditions; (4) choice of model structure (decision models vs. Markov models) is complex; decision models with shorter time periods are useful due to the paucity of data on long-term outcomes, but Markov models of disease progression are better at capturing the cyclical nature of depression; (5) health states in disease progression models are not well-defined; (6) transition probabilities between health states are difficult to obtain from literature; and (7) novel interventions must compete against relatively low-cost generic pharmacological treatments, such as selective serotonin reuptake inhibitors.