Inference for max-linear Bayesian networks with noise
This work addresses causal inference for extreme-value data, but it appears incremental as it builds on existing MLBN frameworks with noise.
The paper tackles the problem of causal inference in extreme-value settings using Max-Linear Bayesian Networks with noise, showing that edge parameter estimators are normally distributed and conducting computational experiments with EM and quadratic optimization.
Max-Linear Bayesian Networks (MLBNs) provide a powerful framework for causal inference in extreme-value settings; we consider MLBNs with noise parameters with a given topology in terms of the max-plus algebra by taking its logarithm. Then, we show that an estimator of a parameter for each edge in a directed acyclic graph (DAG) is distributed normally. We end this paper with computational experiments with the expectation and maximization (EM) algorithm and quadratic optimization.