Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification

DOI10.1198/JASA.2009.0012zbMath1388.62074OpenAlexW2081566815MaRDI QIDQ5256115

Donald B. Rubin, Fabrizia Mealli, Junni L. Zhang

Publication date: 22 June 2015

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1198/jasa.2009.0012

zbMATH Keywords

monotonicity assumptionRubin causal modeltreatment effect on wages

Mathematics Subject Classification ID

Applications of statistics to social sciences (62P25) Parametric inference (62F99)

Related Items (16)

Estimation of Causal Effects in Latent Strata with an Encouragement for Response ⋮ Front-Door Versus Back-Door Adjustment With Unmeasured Confounding: Bias Formulas for Front-Door and Hybrid Adjustments With Application to a Job Training Program ⋮ Identifiability of causal effects on a binary outcome within principal strata ⋮ Estimation of Heterogeneous Individual Treatment Effects With Endogenous Treatments ⋮ Evaluating the Effect of Training on Wages in the Presence of Noncompliance, Nonemployment, and Missing Outcome Data ⋮ Identification of Local Causal Effects with Missing Outcome Values and an Instrument for Non Response ⋮ Sensitivity of the bounds on the ATE in the presence of sample selection ⋮ Causal inference: a missing data perspective ⋮ The fragility of standard inferential approaches in principal stratification models relative to direct likelihood approaches ⋮ Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition ⋮ Local average and quantile treatment effects under endogeneity: a review ⋮ Survivor-Complier Effects in the Presence of Selection on Treatment, With Application to a Study of Prompt ICU Admission ⋮ Augmented Designs to Assess Principal Strata Direct Effects ⋮ Identification of causal effects within principal strata using auxiliary variables ⋮ Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance ⋮ Instrumental variables: an econometrician's perspective

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