… We propose constrained causal Bayesian optimization (cCBO), an approach for finding
interventions in a known causal graph that optimize a target variable under some constraints. …

… Bayesian optimization for constrained problems in the general case that noise may be present
in the constraint functions, and the objective and constraints … This is a causality dilemma: …

… -causal and non-adversarial Bayesian optimization methods on … 3There may be constraints
on the actions our agent can take. … ) for how our setup can be extended to handle constraints. …

… in causal networks that is suitable when application-specific or analysis-specific constraints
should inform such inference or when little or no data for the learning of causal network …

… constraints. Therefore we construct a FIR filter whose parameters are optimized through
Bayesian Optimization with constraints. … and compliance with the constraints during the learning …

… We propose a novel Bayesian optimizationbased method inspired by Bayes factors that aims
to maximize the probability of obtaining decisive and correct evidence. Our approach uses …

… weights are the best to estimate the causal effect, if one agrees a priori with the objective
function and the constraints. In addition, these optimization-based methods generally allow to …

… the offline Causal Global Optimization problem [2] on hybrid Bayesian networks … Causal
Global Optimization problem [2], which has been further extended to the dynamic [1], constrained …

… causal structures that correspond to a given measurement timescale structure. We provide
a constraint … the first constraint optimization approach for recovering the system timescale …

… for differentiable Bayesian optimal experimental design for causal discovery. Our method
allows not only for single-target but also various multi-target (constrained and unconstrained) …