Sharp bounds on the causal effects in randomized experiments with ``truncation-by-death

DOI10.1016/j.spl.2007.05.015zbMath1131.62099OpenAlexW2058330051MaRDI QIDQ2476822

Kosuke Imai

Publication date: 12 March 2008

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2007.05.015

zbMATH Keywords

identificationrandomized clinical trialscausal inferenceprincipal stratificationaverage treatment effectquantile treatment effectdirect and indirect effect

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Survival analysis and censored data (62N99)

Related Items (12)

Identifiability of Causal Effects for Binary Variables with Baseline Data Missing Due to Death ⋮ Identifying marginal treatment effects in the presence of sample selection ⋮ Wilcoxon-Mann-Whitney statistics in randomized trials with non-compliance ⋮ Simple relations between principal stratification and direct and indirect effects ⋮ Identification of causal effects in the presence of nonignorable missing outcome values ⋮ Causal inference: a missing data perspective ⋮ Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes ⋮ Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition ⋮ 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

Cites Work

• Principal Stratification in Causal Inference
• Bounds on Causal Effects in Three-Arm Trials With Non-Compliance
• Statistics and Causal Inference
• Direct and Indirect Causal Effects via Potential Outcomes*
• Identification and Robustness with Contaminated and Corrupted Data
• Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings

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