Invariant Causal Set Covering Machines
Keywords: invariance, environments, rule-based models, learning algorithms, machine learning
TL;DR: We propose Invariant Causal Set Covering Machines, an extension of a classical learning algorithm for rule-based models, that relies on ideas from the ICP literature to efficiently construct models that avoid spurious associations.
Abstract: Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce such models are often vulnerable to spurious associations and thus, they are not guaranteed to extract causally-relevant insights. In this work, we build on ideas from the invariant causal prediction literature to propose Invariant Causal Set Covering Machines, an extension of the classical Set Covering Machine algorithm for conjunctions/disjunctions of binary-valued rules that provably avoids spurious associations. We demonstrate both theoretically and empirically that our method can identify the causal parents of a variable of interest in polynomial time.
Submission Number: 19
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