Comparison of two competing<unk> s (DCM, GCM) used for interpretation of fMRI images In the philosophy of science, a<unk>  (or structural<unk>  is a conceptual model that describes the<unk>  mechanisms of a system.<unk> s can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for. They can allow some questions to be answered from existing observational data without the need for an interventional study such as a randomized controlled trial. Some interventional studies are inappropriate for ethical or practical reasons, meaning that without a<unk> , some hypotheses cannot be tested.<unk> s can help with the question of external validity (whether results from one study apply to unstudied populations).<unk> s can allow data from multiple studies to be merged (in certain circumstances) to answer questions that cannot be answered by any individual data set.<unk> s are falsifiable, in that if they do not match data, they must be rejected as invalid. They must also be credible to those close to the phenomena the model intends to explain.<unk> s have found applications in signal processing, epidemiology and machine learning.
Causal model
Biomedical model
Causal graph
Mixing study
Agent study
Platform trial
Solidarity trial
Normalization model