The core of addiction science is the conceptualization, development, testing, and dissemination of increasingly effective treatments for alcohol and substance use disorders (SUD). This dissertation presents contributions at multiple stages of this developmental process, using three high-impact tools to address novel, timely research questions. Paper 1 addresses the emerging field of support services for people in long-term recovery. In the early stages of treatment development, high-quality descriptive research designs using large datasets provide foundational information about exactly what needs treating. This project examined impairment in several domains of everyday functioning, including self-care, work performance, and social relationships. A key contribution was testing multiple potential drivers of impairment – including concurrent mental illness and social determinants of health – to avoid the stigmatizing assumption that all problems experienced during remission are caused by residual effects of SUD itself. Indeed, results point toward a mental-health focused agenda for treatment after one year of recovery. Rather than defining treatment targets, Paper 2 takes a conceptual, theory-driven approach to examine why harm reduction strategies may be ineffective at achieving well-defined harm reduction goals: reducing total alcohol use and high-risk drinking behaviors. By applying fuzzy-trace theory, we find evidence that low-risk drinkers likely think about alcohol-related risk using gist-based, categorical thinking, rather than taking a “trading off” approach to alcohol risk, wherein large amounts of alcohol are deliberately substituted with small amounts. This thinking is not about avoiding alcohol altogether, although such thinking would produce less risk taking. Results provide an empirical foundation for the development of intervention strategies aimed at training gist-based thinking about alcohol use. Project 3 aims to facilitate greater dissemination and uptake of an emerging treatment strategy known as peer recovery support services. Services with greater evidence of cost-effectiveness are more likely to be supported by insurance funding. This project combines the use of two existing datasets and machine learning to overcome a practical obstacle to estimating PRSS cost-effectiveness. Using the algorithm that resulted from this work, researchers can conduct cost-effectiveness analyses of prior clinical trials on PRSS even when a utility-compatible measure was not administered during data collection.