Primary care interventions, including new primary care policies or quality improvement programs, are often evaluated without the use of randomized controlled trials (RCTs), as randomizing who receives the intervention can be infeasible for many practical, ethical and political reasons (1). In these cases, evidence on the effect of the intervention must stem from non-randomized studies (eg quasiexperimental studies, natural experiments or observational studies) which presents many complexities to isolating the causal effect from the many sources of bias and threats to validity, including concurrent events, lack of comparability across groups, selection bias, etc. Faced with these barriers, researchers often conservatively accept that determining causal effects in such non-randomized settings is unattainable and have become complacent with claims of ‘association’rather than ‘causation.’Recent methodological developments in the causal inference literature, however, have shown that, if specific conditions hold, the causal effect of non-randomized interventions can still be reliably estimated (2, 3). These advancements represent a paradigm shift in how we approach omnipresent causal questions, opening up the possibility of making causal claims even with non-randomized data. Methods developed under this causal framework are becoming increasingly used in many other fields, including epidemiology (4), pharmacosurveillance (5, 6) and health economics (7), but have yet to permeate into mainstream primary care research. Given that many primary care studies are conducted outside the randomized setting, causal inference methods offer enormous potential to this field including applications in practicebased research, health services research, pragmatic trials and quality improvement initiatives.