Mechanisms, Capacities, and Dependencies: A New Theory of Causal Reasoning

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Causal cognition endows humans with impressive abilities. It allows us to predict and explain, plan actions, understand confoundings, make educated guesses about transfer situations, think about counterfactual alternatives, and use and engineer innovative devices. No present psychological theory can provide a complete account of all these competencies within an integrated framework. Moreover, partial theories focusing on specific tasks tend to neglect interactions between competencies. Another shortcoming is that most theories ignore or downplay the important role of background knowledge about mechanisms. In the past years, more advanced theories have been proposed in philosophy and computer science that acknowledge some of these deficits. One recent development is Pearl's theory of causation. Pearl has changed his earlier view that causation can be reduced to covariations. He argues now that abstract structural causal models encoding unobserved mechanisms in terms of functional dependencies guide causal queries. However, modeling mechanisms as dependencies has been criticized by philosophers working on the so-called New Mechanism view, which has been developed to analyze mechanism representations in a variety of domains. For example, the philosopher Cartwright has argued that statistical dependencies in the natural sciences are discovered in the context of so-called nomological machines. Nomological machines, such as a pendulum, are non-causal devices that consist of interconnected components with specific capacities. Other salient examples are artifacts, such as cars or robots. Artifacts are designed to produce covariations. These can be represented by a Bayes net, but to provide a causal understanding, it is necessary to understand how the covariations are generated by the underlying nomological machine. Both frameworks, Pearl's theory and the New Mechanism approach, have limitations: Whereas Pearl's theory only provides an impoverished model of mechanism knowledge in terms of functional dependencies between variables, the New Mechanism view exaggerates what laypeople know. Moreover, a formal theory integrates dependency knowledge and mechanism representations is lacking within both approaches. A new theory (MEC-DEP) will be developed that explains the breadth and flexibility of causal cognition as a product of the interaction between mechanism and dependency knowledge. MEC-DEP will be experimentally tested by investigating different causal models, mechanisms, and tasks in various content domains and populations (adults, children, non-human primates). Additionally, computational models of MEC-DEP will be developed and the role of mechanisms in psychological research will be analyzed. The new theory will fundamentally change our thinking about causality in the many areas that can profit from a deeper understanding of the normative and descriptive basis of causal inferences.