Not involved with causal.app, but I see it as going the other way as far as causality goes. Pearl-style models are trying to infer causation from data. Here, you give it a causal model that encapsulates how you think a scenario works (which variables affect the outcome, how, and a range of likely values for them), and it simulates your model to give you ranges of likely outcomes, plus some other things like sensitivity analysis (which variables impact likely outcomes the most). I like the comparison they make to spreadsheet models. It's that style of modeling but with Monte Carlo simulations, so you can put ranges instead of single numbers in cells, and the ranges propagate through the model to output ranges.
