  Recent developments in structural equation modeling have produced several
methods that can usually distinguish cause from effect in the two-variable
case. For that purpose, however, one has to impose substantial structural
constraints or smoothness assumptions on the functional causal models. In this
paper, we consider the problem of determining the causal direction from a
related but different point of view, and propose a new framework for causal
direction determination. We show that it is possible to perform causal
inference based on the condition that the cause is "exogenous" for the
parameters involved in the generating process from the cause to the effect. In
this way, we avoid the structural constraints required by the SEM-based
approaches. In particular, we exploit nonparametric methods to estimate
marginal and conditional distributions, and propose a bootstrap-based approach
to test for the exogeneity condition; the testing results indicate the causal
direction between two variables. The proposed method is validated on both
synthetic and real data.
