Abstract
Many models of evolution are implicitly causal processes. Features such as causal feedback between evolutionary variables and evolutionary processes acting at multiple levels, though, mean that conventional causal models miss important phenomena. We develop here a general theoretical framework for analyzing evolutionary processes drawing on recent approaches to causal modeling developed in the machine-learning literature, which have extended Pearls do-calculus to incorporate cyclic causal interactions and multilevel causation. We also develop information-theoretic notions necessary to analyze causal information dynamics in our framework, introducing a causal generalization of the Partial Information Decomposition framework. We show how our causal framework helps to clarify conceptual issues in the contexts of complex trait analysis and cancer genetics, including assigning variation in an observed trait to genetic, epigenetic and environmental sources in the presence of epigenetic and environmental feedback processes, and variation in fitness to mutation processes in cancer using a multilevel causal model respectively, as well as relating causally-induced to observed variation in these variables via information theoretic bounds. In the process, we introduce a general class of multilevel causal evolutionary processes which connect evolutionary processes at multiple levels via coarse-graining relationships. Further, we show how a range of fitness models can be formulated in our framework, as well as a causal analog of Prices equation (generalizing the probabilistic Rice equation), clarifying the relationships between realized/probabilistic fitness and direct/indirect selection. Finally, we consider the potential relevance of our framework to foundational issues in biology and evolution, including supervenience, multilevel selection and individuality. Particularly, we argue that our class of multilevel causal evolutionary processes, in conjunction with a minimum description length principle, provides a conceptual framework in which identification of multiple levels of selection may be reduced to a model selection problem.
Highlights
Causality is typically invoked in accounts of evolutionary processes
As we show, using this model, the causal information decomposition (CID) described in “Cyclic and multilevel causality in biology” and Appendix A provides a principled framework for breaking down the variation in a complex trait due to genetic, epigenetic and environmental factors; 1 3
Where all II, Causal Information Decomposition (CID) and PID quantities are evaluated in the eq-CTCM* model
Summary
Causality is typically invoked in accounts of evolutionary processes. For instance, for a variant to be subject to direct selection, it is necessary that it has a causal impact on fitness. Hierarchy is ubiquitous in evolution, and many phenomena, such as multicellularity and eusociality, seem to require a multilevel selection framework for analysis, implicitly invoking causal processes at multiple levels (Okasha 2006). (PID) (Bertschinger et al 2014; Griffith and Koch 2014b; Williams and Beer 2010) Such definitions, can only be applied in causal models with a DAG structure, leaving open the question of how causal information should be defined and decomposed in a system with cyclic interactions. We propose a general causal framework for formulating models of evolutionary processes which allows for cyclic interactions between evolutionary variables and multiple causal levels, drawing on the transformation framework of Rubenstein et al (2017) as described above. A requirement of this framework is that causal relationships between variables must form a Directed
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