Causal Abstraction with Lossy Representations

This academic paper introduces projected abstractions, a novel framework designed to enhance causal inference in artificial intelligence systems by accommodating lossy representations. Traditional causal abstraction methods, which simplify complex "low-level" causal models into more manageable "high-level" ones, often fail when multiple low-level interventions map to the same high-level intervention but produce different effects, a limitation known as the Abstract Invariance Condition (AIC). The authors propose projected abstractions to overcome this by reinterpreting high-level quantities as distributions over corresponding low-level quantities, even when the AIC is violated. They present an algorithm to construct these abstractions and introduce a partially projected C-DAG as a new graphical tool to identify and estimate high-level causal queries from limited low-level data, demonstrating its effectiveness in high-dimensional image settings.