Multi-Level Cause-Effect Systems
This work addresses the challenge of aggregating causal features from vast micro-measurements in fields like neuroscience, though it appears incremental as it generalizes prior methods.
The authors tackled the problem of discovering macro-level causal relations from micro-level measurements when the macro-level effect is unspecified, presenting a domain-general framework and algorithm for identifying such causal structures, with validation on a simulated neuroscience experiment.
We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal features are poorly understood and have to be aggregated from vast arrays of micro-measurements. Our approach generalizes that of Chalupka et al. (2015) to the setting in which the macro-level effect is not specified. We formalize the connection between micro- and macro-variables in such situations and provide a coherent framework describing causal relations at multiple levels of analysis. We present an algorithm that discovers macro-variable causes and effects from micro-level measurements obtained from an experiment. We further show how to design experiments to discover macro-variables from observational micro-variable data. Finally, we show that under specific conditions, one can identify multiple levels of causal structure. Throughout the article, we use a simulated neuroscience multi-unit recording experiment to illustrate the ideas and the algorithms.