Need for causal modeling approximations

Many scientific studies seek to discover cause-effect relationships among observed variables of interest. Causal modeling and causal discovery are central to science. In order to algorithmically consider causal relations, the relations must be placed into a representation structure or model that supports manipulation and discovery. Knowledge of at least some causal effects is inherently imprecise or approximate. An algorithmic way of handling causal imprecision is needed. There are several needs of a causal model; this paper describes many of the causal representation needs.