  Recent work on dynamic interventions has greatly expanded the range of causal
questions researchers can study while weakening identifying assumptions and
yielding effects that are more practically relevant. However, most work in
dynamic interventions to date has focused on settings where we directly alter
some unconfounded treatment of interest. In policy analysis, decision makers
rarely have this level of control over behaviors or access to experimental
data. Instead, they are often faced with treatments they can affect only
indirectly and whose effects must be learned from observational data. In this
paper, we propose new estimands and estimators of causal effects based on
dynamic interventions with instrumental variables. This method does not rely on
parametric models and does not require an experiment. Instead, we estimate the
effect of a dynamic intervention on the instrument. This robustness should
reassure policy makers that these estimates can be used to effectively inform
policy. We demonstrate the usefulness of this estimation strategy in a case
study examining the effect of visitation on recidivism.
