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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

There has been an increase in interest in experimental evaluations toestimate causal effects, partly because their internal validity tends to behigh. At the same time, as part of the big data revolution, large, detailed,and representative, administrative data sets have become more widely available.However, the credibility of estimates of causal effects based on such data setsalone can be low.  In this paper, we develop statistical methods for systematically combiningexperimental and observational data to obtain credible estimates of the causaleffect of a binary treatment on a primary outcome that we only observe in theobservational sample. Both the observational and experimental samples containdata about a treatment, observable individual characteristics, and a secondary(often short term) outcome. To estimate the effect of a treatment on theprimary outcome while addressing the potential confounding in the observationalsample, we propose a method that makes use of estimates of the relationshipbetween the treatment and the secondary outcome from the experimental sample.If assignment to the treatment in the observational sample were unconfounded,we would expect the treatment effects on the secondary outcome in the twosamples to be similar. We interpret differences in the estimated causal effectson the secondary outcome between the two samples as evidence of unobservedconfounders in the observational sample, and develop control function methodsfor using those differences to adjust the estimates of the treatment effects onthe primary outcome.  We illustrate these ideas by combining data on class size and third gradetest scores from the Project STAR experiment with observational data on classsize and both third and eighth grade test scores from the New York schoolsystem.

Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes