Cyclic Counterfactuals under Shift-Scale Interventions
This work addresses the problem of handling feedback loops in causal inference for domains such as biology, but it appears incremental as it extends existing frameworks to cyclic models without claiming major breakthroughs.
The paper tackles counterfactual inference in cyclic structural causal models, which are common in real-world systems like biology, by studying shift-scale interventions that modify variable mechanisms through rescaling and shifting.
Most counterfactual inference frameworks traditionally assume acyclic structural causal models (SCMs), i.e. directed acyclic graphs (DAGs). However, many real-world systems (e.g. biological systems) contain feedback loops or cyclic dependencies that violate acyclicity. In this work, we study counterfactual inference in cyclic SCMs under shift-scale interventions, i.e., soft, policy-style changes that rescale and/or shift a variable's mechanism.