Multi-Level Cause-Effect Systems

By Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona. 2015

64 citations recorded by the source corpus.

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.

Subfields: Causal Inference & Discovery

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Citations in the corpus

Toward Causal Representation Learning (2021, background): “Although causal models in economics, medicine, or psychology often use variables that are abstractions of underlying quantities, it is challenging to state general conditions under which coarse-grained variables admit causal models with well-deﬁned interventions [42], [208].”
Towards Causal Representation Learning (2021, background): “medicine, or psychology often use variables that are abstractions of underlying quantities, it is challenging to state general conditions under which coarse-grained variables admit causal models with well-defined interventions [41, 207].”
Causality for Machine Learning (2019, background): “Although every causal models in economics, medicine, or psychology uses variables that are abstractions of more elementary concepts, it is challenging to state general conditions under which coarse-grained variables admit causal models with well-defined interventions (Chalupka et al., 2015; Rubenste”
UvA-DARE ( Digital Academic Repository ) Causal Consistency of Structural Equation Models (2017, unknown): “(b) Micro-level models versus macro-level models in which the macro-variables are aggregate features of the micro-variables [4, 5, 9, 11, 17]; e. g. instead of modelling the brain as consisting of 100 billion neurons it can be modelled as averaged neuronal activity in distinct functional brain…”
Causal feature learning: an overview (2017, unknown): “For example, in the wind-temperature climate system discussed in Chalupka et al. (2016a) (see also Sect.”
Causal Abstractions of Neural Networks (2021, methodology): “To concretize this, we turn to formal, broadly interventionist theories of causality [25, 19], in which causal processes are characterized by effects of interventions, and theories of abstraction [1, 2, 5, 22] where relationships between two causal processes are determined by the presence of systema”