mlsanityData to 7 Oct 2026

Identifying the Causes of Pyrocumulonimbus (PyroCb)

arXiv 2211.08883 ↗First posted 16 Nov 2022stat.ML cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 2 of its 36 references that are in the map. First posted on arXiv 16 Nov 2022.

Builds on

2 of 36 references.

References · 2

  1. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  2. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 2.

4 papers.