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

Instrumental variables have been widely used to estimate the causal effect ofa treatment on an outcome. Existing confidence intervals for causal effectsbased on instrumental variables assume that all of the putative instrumentalvariables are valid; a valid instrumental variable is a variable that affectsthe outcome only by affecting the treatment and is not related to unmeasuredconfounders. However, in practice, some of the putative instrumental variablesare likely to be invalid. This paper presents a simple and general approach toconstruct a confidence interval that is robust to possibly invalid instruments.The robust confidence interval has theoretical guarantees on having the correctcoverage and can also be used to assess the sensitivity of inference wheninstrumental variables assumptions are violated. The paper also shows that therobust confidence interval outperforms traditional confidence intervals popularin instrumental variables literature when invalid instruments are present. Thenew approach is applied to a developmental economics study of the causal effectof income on food expenditures.

A simple and robust confidence interval for causal effects with possibly invalid instruments