Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models
This work addresses computational bottlenecks in causal inference for researchers and practitioners, offering incremental improvements in efficiency and application.
The paper tackled the computational challenges of calculating probabilities of causation due to partial identifiability and latent confounding, introducing algorithmic simplifications that significantly reduce complexity and a novel framework for Root Cause Analysis that uses these metrics to rank causal paths.
Probabilities of causation provide principled ways to assess causal relationships but face computational challenges due to partial identifiability and latent confounding. This paper introduces both algorithmic simplifications, significantly reducing the computational complexity of calculating tighter bounds for these probabilities, and a novel methodological framework for Root Cause Analysis that systematically employs these causal metrics to rank entire causal paths.