Workshop: Causal Machine Learning for Real-World Impact

Identifying causes of Pyrocumulonimbus (PyroCb)

Emiliano Diaz · Kenza Tazi · Ashwin Braude · Daniel Okoh · Kara Lamb · Duncan Watson-Parris · Paula Harder · Nis Meinert

Abstract: 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 $Y \indep E|X$ 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 850hPa, a component of wind at 250 hPa, 13.3 \textmu m thermal emissions, convective available potential energy and altitude.

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