Background: Alien species are severely impacting the environment, public health and socioeconomy at a global scale. Their management is thus of crucial importance and the subject of intensive research efforts. Common ragweed Ambrosia artemisiifolia L. is an alien species with negative impacts on agriculture, human health and biodiversity. It is a highly allergenic, wind-pollinated herb native to North America that was first introduced to Europe during the seventeenth century. It has since become widespread and is currently in an ongoing phase of rapid spread and increasing abundance. Several management approaches are currently implemented and effective control of the species can have strong socioeconomic benefits. However, evidence for management effectiveness is scattered and has not yet been synthesised systematically. For these reasons, we here aim to systematically review the evidence to assess (a) what is the effectiveness of management options used for control of Common ragweed Ambrosia artemisiifolia and (b) what is the effect of confounding factors such as habitat, climate and frequency and timing of treatment? Methods: This protocol specifies the methods for conducting a systematic review to answer the specified questions. Search terms relating to the population and the intervention (type of management) will be combined and searched in a range of databases and other sources. Specific inclusion criteria are (i) any population of Ambrosia artemisiifolia at any habitat including populations in agricultural settings and such used for experimental research at any geographic location (including its native range), (ii) any physical, chemical, biological or combined management action; (iii) direct outcome measures including change in coverage, abundance, biomass, survival, reoccurrence, biology (e.g. growth, height, leaf area) or pollen production. The wide range of quality of primary literature will be evaluated with a tailored system for assessing susceptibility to bias and the reliability of the studies. If extracted data are suitable for quantitative synthesis, we aim to calculate effect sizes and conduct meta-analyses.