Invariant Causal Set Covering Machines
This addresses the need for interpretable models that reliably extract causal insights, though it is incremental as it builds on existing invariant causal prediction and Set Covering Machine methods.
The authors tackled the problem of rule-based models being vulnerable to spurious associations by proposing Invariant Causal Set Covering Machines, which provably avoids such issues and can identify causal parents of a variable in polynomial time.
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.