  Studying solar wind conditions is central to forecasting impact of space
weather on Earth. Under the assumption that the structure of this wind is
constant in time and corotates with the Sun, solar wind and thereby space
weather forecasts have been made quite effectively. Such corotation forecasts
are well studied with decades of observations from STEREO and near-Earth
spacecrafts. Forecast accuracy depends upon the latitudinal separation (or
offset $\Delta \theta$) between source and spacecraft, forecast lead time
($\Delta t$) and the solar cycle via the sunspot number (SSN). The precise
dependencies factoring in uncertain- ties however, are a mixture of influences
from each of these factors. And for high precision forecasts, it is important
to understand what drives the forecast accuracy and its uncertainty. Here we
present a causal inference approach based on information theoretic measures to
do this. Our framework can compute not only the direct (linear and non-linear)
dependencies of the forecast mean absolute error (MAE) on SSN, $\Delta t$ and
$\Delta t$, but also how these individual variables combine to enhance or
diminish the MAE. We provide an initial assessment of this with potential of
aiding data assimilation in the future.
