English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently, there has been a surge in methodological development for thedifference-in-differences (DiD) approach to evaluate causal effects. Standardmethods in the literature rely on the parallel trends assumption to identifythe average treatment effect on the treated. However, the parallel trendsassumption may be violated in the presence of unmeasured confounding, and theaverage treatment effect on the treated may not be useful in learning atreatment assignment policy for the entire population. In this article, wepropose a general instrumented DiD approach for learning the optimal treatmentpolicy. Specifically, we establish identification results using a binaryinstrumental variable (IV) when the parallel trends assumption fails to hold.Additionally, we construct a Wald estimator, novel inverse probabilityweighting (IPW) estimators, and a class of semiparametric efficient andmultiply robust estimators, with theoretical guarantees on consistency andasymptotic normality, even when relying on flexible machine learning algorithmsfor nuisance parameters estimation. Furthermore, we extend the instrumented DiDto the panel data setting. We evaluate our methods in extensive simulations anda real data application.

A Semiparametric Instrumented Difference-in-Differences Approach to Policy Learning