  Many methods of estimating causal models do not provide estimates of
confidence in the resulting model. In this work, a metric is proposed for
validating the output of a causal model fit; the robustness of the model
structure with resampled data. The metric is developed for time series causal
models, but is also applicable to non-time series data. The proposed metric may
be utilized regardless of the method selected for fitting the causal model. We
find that with synthetically generated data, this metric is able to
successfully identify the true data generating model in most cases.
Additionally, the metric provides both a qualitative measure of model
confidence represented by the robustness level as well as accurate estimates of
uncertainty in model coefficients which are important in interpreting model
results. The use of this metric is demonstrated on both numerically simulated
data and a case study from existing causal model literature.
