Andrew Ellis

Working papers

  • Subjective Causality in Choice, with H. C. Thysen.
    Abstract. An agent makes a stochastic choice from a set of lotteries. She infers the outcomes of her options using a subjective causal model represented by a directed acyclic graph, and consequently may misinterpret correlation as causation. Her choices affect her inferences which in turn affect her choices, so the two together must form a personal equilibrium. We show how an analyst can identify the agent's subjective causal model from her random choice rule. In addition, we provide necessary and sufficient conditions that allow an analyst to test whether the agent's behavior is compatible with the model.

  • Identifying Assumptions and Research Dynamics, with R. Spiegler. [slides]
    Abstract. Abstract. A representative researcher has repeated opportunities for empirical research. To process findings, she must impose an "identifying assumption." She conducts research when the assumption is sufficiently plausible (taking into account both current beliefs and the quality of the opportunity), and updates beliefs as if the assumption were perfectly valid. We study the dynamics of this learning process. While the rate of research cannot always increase over time, research slowdown is possible. We characterize environments in which the rate is constant. Long-run beliefs can exhibit history-dependence and "false certitude." We apply the model to stylized examples of empirical methodologies: experiments, various causal-inference techniques, and "calibration."
  • Correlation Concern. [slides]
    Revision requested by the Journal of Economic Theory
    Abstract. In many choice problems, the interaction between several distinct variables determines the payoff of each alternative. I propose and axiomatize a model of a decision maker who recognizes that she may not accurately perceive the correlation between these variables, and who takes this into account when making her decision. She chooses as if she calculates each alternative's expected outcome under multiple possible correlation structures, and then evaluates it according to the worst expected outcome.
  • Misspecified Higher-Order Beliefs and Failures of Social Learning, with M.R. Levy and B. Szentes [pdf coming soon].
    Abstract. We study a model of social learning with misspecified higher-order beliefs. The environment is such that with rational beliefs, players' actions would converge to the public information optimal action. A small misperception at an arbitrarily high level of the belief hierarchy may lead to predetermined learning: agents become arbitrarily convinced that a given state obtains, independently of the true state of the world.