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I don't think WFC can be described as an example of a Monte Carlo method.
In a Monte Carlo experiment, you use randomness to approximate a solution, for example to solve an integral where you don't have a closed form. The more you sample, the more accurate the result.
In WFC, the number of random experiments depends on your map size and is not variable.
Sorry, I should have been more specific - it's an application of Markov Chain Monte Carlo. You define a chain and randomly evaluate it until you're done - is there anything beyond this in WFC?
I'm not an expert on Monte Carlo methods, but reading the Wikipedia article on Markov Chain Monte Carlo, this doesn't fit what WFC does for the reasons I mentioned above. In MCMC, your get a better result by taking more steps, in WFC, the number of steps is given by the map size, it can't be changed.
I'm not talking about repeated application of MCMC, just a single round. In this single round, the number of steps is also given by the map size.