Results on the PASCAL challenge "Simple causal effects in time series"
In: NIPS 2008 Workshop on Causality, 12 December 2008, Whistler, Canada.
A solution to the PASCAL challenge ``Simple causal effects in time series'' (www.causality.inf.ethz.ch) is presented. The data is modeled as a sum of a constant-plus-sin term and a term that is a linear function of a small number of inputs. The problem of identifying such a model from the data is nonconvex in the frequency and phase parameters of the sin and is combinatorial in the number of inputs. The proposed method is suboptimal and exploits several heuristics. First, the problem is split into two phases: 1) identification of the autonomous part and 2) identification of the input dependent part. Second, local optimization method is used to solve the problem in the first phase. Third, l1 regularization is used in order to find a sparse solution in the second phase.