Targeted Reduction of Causal Models
Armin Kekić Bernhard Schölkopf Michel Besserve
Why does a phenomenon occur? Addressing this question is central to most
scientific inquiries based on empirical observations, and often heavily relies
on simulations of scientific models. As models become more intricate,
deciphering the causes behind these phenomena in high-dimensional spaces of
interconnected variables becomes increasingly challenging. Causal machine
learning may assist scientists in the discovery of relevant and interpretable
patterns of causation in simulations. We introduce Targeted Causal Reduction
(TCR), a method for turning complex models into a concise set of causal factors
that explain a specific target phenomenon. We derive an information theoretic
objective to learn TCR from interventional data or simulations and propose
algorithms to optimize this objective efficiently. TCR's ability to generate
interpretable high-level explanations from complex models is demonstrated on
toy and mechanical systems, illustrating its potential to assist scientists in
the study of complex phenomena in a broad range of disciplines.