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

Combining short-term experimental data with observational data enablescredible long-term policy evaluation. The literature offers two key butnon-nested assumptions, namely the latent unconfoundedness (LU; Athey et al.,2020) and equi-confounding bias (ECB; Ghassami et al., 2022) conditions, tocorrect observational selection. Committing to the wrong assumption leads tobiased estimation. To mitigate such risks, we provide a novel bracketingrelationship (cf. Angrist and Pischke, 2009) repurposed for the setting withdata combination: the LU-based estimand and the ECB-based estimand serve as thelower and upper bounds, respectively, with the true causal effect lying inbetween if either assumption holds. For researchers further seeking pointestimates, our Lalonde-style exercise suggests the conservatively more robustLU-based lower bounds align closely with the hold-out experimental estimatesfor educational policy evaluation. We investigate the economic substantives ofthese findings through the lens of a nonparametric class of selectionmechanisms and sensitivity analysis. We uncover as key the sub-martingaleproperty and sufficient-statistics role (Chetty, 2009) of the potentialoutcomes of student test scores (Chetty et al., 2011, 2014).

A Bracketing Relationship for Long-Term Policy Evaluation with Combined Experimental and Observational Data