BACKGROUND: Dementia presents complex challenges for causal inference due to its multifactorial aetiology and slow, heterogeneous progression. Randomized controlled trials are often limited in their potential to fully address these challenges because of ethical and practical constraints. As the field evolves, observational studies incorporating advanced causal inference methods are increasingly used to estimate real-world effects in dementia research. However, the implementation of these methods varies widely and has not been systematically evaluated, with an emerging trend towards integration with techniques such as machine learning. This systematic review will critically examine how causal inference techniques are applied in dementia research, assess their methodological rigor, and identify trends, assumptions, and gaps that may inform future applications and methodological innovation in the field. METHODS: Following PRISMA guidelines, searches will be conducted in MEDLINE, EMBASE, Web of Science, PsycINFO, Scopus, and the Cochrane Library for studies published between 1960 and 2024. Eligible studies will include observational designs that use causal inference methods to investigate outcomes such as cognitive decline, disease progression, and quality of life. Data extraction will capture study characteristics, methodological details, and key findings, with risk of bias assessed using ROBINS-I. A narrative synthesis will summarize qualitative results, and meta-analyses will be performed when methodological homogeneity permits. DISCUSSION: This review will address a critical gap in the evaluation of the application of causal inference methods in real-world dementia research. By identifying methodological challenges, underlying assumptions, and emerging analytical techniques, it aims to strengthen research rigor and reproducibility and inform future methodological development, with potential implications for policy and practice in dementia care. SYSTEMATIC REVIEW REGISTRATION: PROSPERO (CRD42024619228).