The main objective of this project is to make causal reasoning an integral part of economic theory. The basic idea is that when people make decisions, they establish a causal mapping from actions to consequences, which is based on an intuitive subjective causal model. For example, when a parent decides how much to invest in his child's education, he may have in mind an intuitive causal model that can be represented by the following graphical diagram: investment in child's education --> child's performance at school --> child's subsequent performance in the labor market. The decision maker then uses this subjective causal model to make sense of statistical regularities in his environment and form beliefs that guide his actions. In the above example, this means that the parent will measure the observed correlation between investment and school performance as well as the observed correlation between school performance and subsequent labor-market performance, and he will combine these correlations in accordance with his causal model. If the decision maker's causal model is incorrect - e.g. when he reverses the true direction of causality between two variables, or when he omits certain confounding variables from his model - then his beliefs may exhibit systematic biases. This in turn can lead to interesting behavioral effects. In the above example, the parent's subjective causal model neglects the possibility that both school and labor-market performance may be affected by unobserved exogenous characteristics of the child. This error may lead to over-investment in the child's education.
In its exploration of behavioral implications of erroneous causal reasoning, this project is related to the behavioral economics and bounded rationality literatures. In particular, it adds a novel dimension to the literature on "non-rational expectations". While behavioral economics has studied a variety of biases in belief formation, it has not yet dealt with the problem of erroneous causal reasoning. However, causal reasoning is fundamental to decision making, especially in modern societies. We are constantly bombarded with observational data and correlations between variables, and whether we are able to draw correct causal conclusions from this type of data is an important question. The motto "correlation does not imply causation" is so well-known precisely because people do tend to draw wrong causal inferences from mere correlations. These errors could make them vulnerable to exploitation by third parties.
From a technical point of view, the project draws on the rich literature in statistics and artificial intelligence (AI) on so-called "Bayesian networks". This literature offers tools that enable us to formalize types of errors of causal reasoning and analyze their behavioral consequences. Thus, if successful, the project may also contribute to the influence of graphical probabilistic models in economics.
