  When inferring the causal effect of one variable on another from
correlational data, a common practice by professional researchers as well as
lay decision makers 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.
