Evaluating Causal Models by Comparing Interventional Distributions
This addresses the need for more reliable evaluation metrics in causal inference, particularly for researchers and practitioners, though it is incremental as it builds on existing methods.
The paper tackles the problem of evaluating causal models by proposing an alternative method that directly measures the accuracy of estimated interventional distributions, showing that traditional structural measures often correspond poorly to this accuracy and can mislead algorithm selection and parameter tuning.
The predominant method for evaluating the quality of causal models is to measure the graphical accuracy of the learned model structure. We present an alternative method for evaluating causal models that directly measures the accuracy of estimated interventional distributions. We contrast such distributional measures with structural measures, such as structural Hamming distance and structural intervention distance, showing that structural measures often correspond poorly to the accuracy of estimated interventional distributions. We use a number of real and synthetic datasets to illustrate various scenarios in which structural measures provide misleading results with respect to algorithm selection and parameter tuning, and we recommend that distributional measures become the new standard for evaluating causal models.