 tells us that the values \(pa_i\) of certain parent variables determine the value \(v_i\) of a child variable according to a function \(f_i\) in the context of a valuation \(u_i\) of background variables. At bottom, a causal model is a set of structural equations. Each equation stands for some causal law or mechanism. We can view these equations as representing elementary causal dependencies. The notion of a parent variable relies on causal graphs, which represent elementary causal relations by directed edges between nodes. Each node stands for a distinct variable of the causal model. Causal models have been used to study both deterministic and probabilistic causation. (For details, see the entry on  causal models.)