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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 andenhances algorithms that learn causal models from data, and this work hasproduced a series of impressive technical advances. However, evaluationtechniques for causal modeling algorithms have remained somewhat primitive,limiting what we can learn from experimental studies of algorithm performance,constraining the types of algorithms and model representations that researchersconsider, and creating a gap between theory and practice. We argue for morefrequent use of evaluation techniques that examine interventional measuresrather than structural or observational measures, and that evaluate thosemeasures on empirical data rather than synthetic data. We survey the currentpractice in evaluation and show that the techniques we recommend are rarelyused in practice. We show that such techniques are feasible and that data setsare available to conduct such evaluations. We also show that these techniquesproduce substantially different results than using structural measures andsynthetic data.

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