Title
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
Abstract
Causal modeling 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 using 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.
Year
Venue
Keywords
2019
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019)
current practice
Field
DocType
Volume
Computer science,Artificial intelligence,Machine learning,Causal model
Conference
32
ISSN
Citations 
PageRank 
1049-5258
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Amanda Gentzel1321.91
Daniel Garant2665.19
David Jensen32648213.30