Bayesian model averaging over the two possible causal directions in a bivariate system is decision-optimal under well-specified models and improves decisions in simulations when structural uncertainty is genuine.
Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects
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Incorporating structural uncertainty in causal decision making
Bayesian model averaging over the two possible causal directions in a bivariate system is decision-optimal under well-specified models and improves decisions in simulations when structural uncertainty is genuine.