Much of the causal discovery literature has focused on identification and estimation of causal models. Less attention has been given to quantifying uncertainty in causal discovery in terms of frequentist confidence statements. In this talk, we will discuss some recent work on forming confidence sets for causal orderings and confidence sets for causal effects when the causal graph is estimated from data.
