Abstract Experimental systems in which phenomena are studied under controlled conditions allow scientists to infer causal relationships from observable effects. When investigating ecosystems, however, scientists face complex systems. The conventional approach is to divide the system into conceptual units and to prepare experimental systems accordingly. Experimental systems are used as models for ecosystems: initially, scientists assume an analogy between the experimental system and ecosystem, then encode the experimental system into a formal system by measuring variables, and decode statements from the formal system to the ecosystem. We distinguish three types of experimental systems, i.e. laboratory, container and field set-ups, further divided into seven subtypes. Starting from the premises of experimental systems, we comment on the possibilities and limitations of experimentally derived causal relationships and on their significance for ecosystem understanding and prediction, illustrated by examples from soil science and the environmental sciences. Experimental set-ups have a characteristic duration, degree of structural integrity, internal variability and boundaries, which relate to conceptual closure and experimental control: control tends to be maximum on short time scales, in homogeneous set-ups with analytical boundaries, and in systems with few parameters to be observed. Complexity is increased at the expense of control. The higher the degree of manipulation, however, the better is reproducibility, but the larger is the deviation from unique ecosystems with their infinite number of factors. The material realization of closed systems is preceded by a conceptual closure of the system. Closure is relative to the domain of phenomena of interest, the theory and the list of variables selected by the scientist. Successful decoding from experimental systems to ecosystems largely depends on the validity of the chosen analogy. Laboratory systems are idealized systems which contain a limited number of a priori defined variables, and which are shielded from environmental influences. In contrast, ecosystems are materially and conceptually open, non-stationary, historical systems, in which system-level properties can emerge, and in which variables are produced internally. We conclude that when conducting experiments, causal factors can be identified, but that causal knowledge derived from insufficiently closed systems is invalid. In ecosystems, innumerous factors interact, which may enhance, reduce or neutralize the effect of an experimentally determined factor. Thus, experimental model systems need to be evaluated for concrete, well-defined ecosystems with a concrete history. Increasingly detailed studies of isolated phenomena in the laboratory will probably not contribute much to ecosystem-level understanding. When conducting experiments, scientists should aim at the maximum degree of complexity they can actually handle and they should justify the chosen analogy.