  A first causal discovery analysis from observational data of pyroCb (storm
clouds generated from extreme wildfires) is presented. Invariant Causal
Prediction was used to develop tools to understand the causal drivers of pyroCb
formation. This includes a conditional independence test for testing $Y$
conditionally independent of $E$ given $X$ for binary variable $Y$ and
multivariate, continuous variables $X$ and $E$, and a greedy-ICP search
algorithm that relies on fewer conditional independence tests to obtain a
smaller more manageable set of causal predictors. With these tools, we
identified a subset of seven causal predictors which are plausible when
contrasted with domain knowledge: surface sensible heat flux, relative humidity
at $850$ hPa, a component of wind at $250$ hPa, $13.3$ micro-meters, thermal
emissions, convective available potential energy, and altitude.
