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.
翻译:介绍了从热电联苯(极端野火产生的暴云)观测数据中得出的首次因果发现分析; 常态原因预测用于开发各种工具,以了解热电联苯形成的原因驱动因素,其中包括有条件独立测试美元,有条件独立测试以美元为单位,以美元为单位,以二进制变量为单位,以美元和多变变量为单位,以美元为单位,连续变量为单位,以美元和欧元为单位,以及贪婪-独立搜索算法,依赖较少的有条件独立测试,以获得更小的、更可管理的因果预测数。有了这些工具,我们找到了7个与领域知识相对照的因果关系预测数:表面合理热通量,相对湿度为850美元,风力部分为250美元,13.3美元微米,热排放,对流潜在能量和高度。