Year: 2020
Type: article
Abstract: Summary Recent work on dynamic interventions has greatly expanded the range of causal questions that researchers can study. Simultaneously, this work has weakened identifying assumptions, yielding effects that are more practically relevant. 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 behaviours or access to experimental data. Instead, they are often faced with treatments that they can affect only indirectly and whose effects must be learned from observational data. 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 treatment induced by a dynamic intervention on an instrument. This robustness should reassure policy makers that these estimates can be used to inform policy effectively. We demonstrate the usefulness of this estimation strategy in a case-study examining the effect of visitation on recidivism.
DOI: 10.1111/rssa.12563
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2898861529