# The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data

@inproceedings{Gentzel2019TheCF, title={The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data}, author={Amanda Gentzel and Dan Garant and David. D. Jensen}, booktitle={NeurIPS}, year={2019} }

Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from experimental studies… CONTINUE READING

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