# Identifying the Causes of Pyrocumulonimbus (PyroCb)

arXiv: https://arxiv.org/abs/2211.08883 · HTML: https://mlsanity.com/paper/2211.08883/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 2 of 2)

- [Causal inference using invariant prediction: identification and confidence intervals](https://mlsanity.com/paper/1501.01332/) (2015)
- [Invariant Causal Prediction for Nonlinear Models](https://mlsanity.com/paper/1706.08576/) (2017)

## Built on by (most cited first, 4 of 4)

- [Model-based causal feature selection for general response types](https://mlsanity.com/paper/2309.12833/) (2023)
- [AI for Extreme Event Modeling and Understanding: Methodologies and Challenges](https://mlsanity.com/paper/2406.20080/) (2024)
- [Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds](https://mlsanity.com/paper/2211.13052/) (2022)
- [Invariant Causal Prediction with Local Models](https://mlsanity.com/paper/2401.05218/) (2024)

## Abstract (arXiv)

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.