Abstract:
This thesis aims to show that explicit understanding of possible causal structures often aids in
inferring the true causes from data. This is done by first understanding that causes are chains
of counterfactual dependence. Insofar as experiments, active or natural are not perfect, data
can easily support false counterfactuals. Even those tools especially designed to identify
unbiased estimates, like instrumental variables, often fail. Causal structure explains the
failure of these tools, but more importantly allows us to better identify which counterfactuals
to reject or accept.
