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

By Amanda Gentzel, Dan Garant, David D. Jensen. 2019

56 citations recorded by the source corpus.

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 of algorithm performance, constraining the types of algorithms and model representations that researchers consider, and creating a gap between theory and practice. We argue for more frequent use of evaluation techniques that examine interventional measures rather than structural or observational measures, and that evaluate those measures on empirical data rather than synthetic data. We survey the current practice in evaluation and show that the techniques we recommend are rarely used in practice. We show that such techniques are feasible and that data sets are available to conduct such evaluations. We also show that these techniques produce substantially different results than using structural measures and synthetic data.

Subfields: Artificial Intelligence, Statistics and Probability, Evaluation & Benchmarks

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Citations in the corpus

Differentiable Causal Discovery from Interventional Data (2020, background): “6, we also report how well the graph can be used to predict the effect of unseen interventions [13].”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, background): “However, as Gentzel et al. (2019) discuss, synthetic data has no “unknown unknowns” and many researcher degrees of freedom, which limits their effectiveness.”