Randomized trials are considered the gold standard for assessing the causal effects of a drug or intervention in a study population, and their results are often utilized in the formulation of health policy. However, there is growing concern that results from trials do not generalize well to their respective target populations, in which policies are enacted, due to substantial demographic differences between study and target populations. In trials related to substance use disorders (SUDs), especially, strict exclusion criteria make it challenging to obtain study samples that are fully ‘representative’ of the populations that researchers wish to generalize their results to. In this study, we look at results from three trials of treatments for methamphetamine dependence and assess their generalizability to well-defined target populations obtained by subsetting the TEDS-A, which contains annual census data on admissions to SUD treatment facilities. Our aim is to implement statistical methods that reweight the study samples to better resemble the target populations in order to obtain more accurate target population-level treatment effect estimates.