Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes

DOI10.1080/01621459.2018.1434530zbMath1398.92009OpenAlexW2807279683WikidataQ90183807 ScholiaQ90183807MaRDI QIDQ4962457

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Publication date: 2 November 2018

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

Full work available at URL: https://doi.org/10.1080/01621459.2018.1434530

zbMATH Keywords

instrumental variablepartial identificationaverage treatment effectinstrumentcausal graphical modelsingle world intervention graph

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Taxonomy, cladistics, statistics in mathematical biology (92B10) Software, source code, etc. for problems pertaining to biology (92-04)

Related Items (9)

On Semiparametric Instrumental Variable Estimation of Average Treatment Effects through Data Fusion ⋮ Mendelian Randomization Mixed-Scale Treatment Effect Robust Identification and Estimation for Causal Inference ⋮ A nonparametric instrumental approach to confounding in competing risks models ⋮ Nonparametric Bounds for Causal Effects in Imperfect Randomized Experiments ⋮ Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects ⋮ The identification region of the potential outcome distributions under instrument independence ⋮ An instrumental variables design for the effect of emergency general surgery ⋮ Discussion of Cui and Tchetgen Tchetgen (2020) and Qiu et al. (2020) ⋮ Machine Intelligence for Individualized Decision Making Under a Counterfactual World: A Rejoinder

Uses Software

• Stata
• TETRAD
• bpbounds
• clrbound

Cites Work

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