Behavioral Causal Inference

• Department of Economics

In this seminar, Ran Spiegler (Tel Aviv University and UCL) will present his paper "Behavioral Causal Inference".

When inferring the causal effect of one variable on another from correlational data, a common practice by professional researchers and lay decision makers alike is to control for some set of exogenous confounding variables. Choosing an inappropriate set of control variables can lead to erroneous causal inferences.

This paper presents a model of lay decision makers who use long-run observational data to learn the causal effect of their actions on a payoff-relevant outcome. Different types of decision makers use different sets of control variables. I obtain upper bounds on the equilibrium welfare loss due to wrong causal inferences, for various families of data-generating processes. The bounds depend on the structure of the type space. When types are "ordered" in a certain sense, the equilibrium condition greatly reduces the cost of wrong causal inference due to poor controls.