  Motivated by Breiman's rousing 2001 paper on the "two cultures" in
statistics, we consider the role that different modeling approaches play in
causal inference. We discuss the relationship between model complexity and
causal (mis)interpretation, the relative merits of plug-in versus targeted
estimation, issues that arise in tuning flexible estimators of causal effects,
and some outstanding cultural divisions in causal inference.
