Indirect Causes in Dynamic Bayesian Networks Revisited

  title={Indirect Causes in Dynamic Bayesian Networks Revisited},
  author={Alexander Motzek and Ralf M{\"o}ller},
Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a “causal design,” i.e., without anticipating indirect influences appropriately in time, we argue that such networks return spurious results. By introducing activator random variables, we… CONTINUE READING