A Non-Parametric Bayesian Method for Inferring Hidden Causes
This work addresses structure learning with hidden causes, potentially useful for domains like medical analysis, but appears incremental as it builds on prior Bayesian methods with a different assumption.
The paper tackled the problem of inferring hidden causes in structure learning by proposing a non-parametric Bayesian method that assumes an unbounded number of hidden causes, with only a finite number influencing observables, and evaluated it on simulated and real medical data.
We present a non-parametric Bayesian approach to structure learning with hidden causes. Previous Bayesian treatments of this problem define a prior over the number of hidden causes and use algorithms such as reversible jump Markov chain Monte Carlo to move between solutions. In contrast, we assume that the number of hidden causes is unbounded, but only a finite number influence observable variables. This makes it possible to use a Gibbs sampler to approximate the distribution over causal structures. We evaluate the performance of both approaches in discovering hidden causes in simulated data, and use our non-parametric approach to discover hidden causes in a real medical dataset.