2016
DOI: 10.1111/1475-6773.12463
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Treatment Effect Estimation Using Nonlinear Two‐Stage Instrumental Variable Estimators: Another Cautionary Note

Abstract: Researchers are increasingly opting for nonlinear 2SRI to estimate treatment effects in models with binary and otherwise inherently nonlinear dependent variables, believing that it produces generally unbiased and consistent estimates. This research shows that positive properties of nonlinear 2SRI rely on assumptions about the relationships between treatment effect heterogeneity and choice.

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Cited by 23 publications
(47 citation statements)
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“…Results from this study conform with the simulation results of Chapman and Brooks (), who compared 2SLS and nonlinear 2SRI with raw residuals in simulation models with binary treatment, binary outcome, and continuous instruments to find that 2SLS produced consistent estimates for the IV effect whereas 2SRI did not reliably estimate either the ATE or the IV effect. However, their study did not examine models with binary instruments, vary rarity of treatment or outcome from approximately 0.5, examine alternative forms of 2SRI residuals, or report coverage probabilities of estimates.…”
Section: Discussionsupporting
confidence: 83%
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“…Results from this study conform with the simulation results of Chapman and Brooks (), who compared 2SLS and nonlinear 2SRI with raw residuals in simulation models with binary treatment, binary outcome, and continuous instruments to find that 2SLS produced consistent estimates for the IV effect whereas 2SRI did not reliably estimate either the ATE or the IV effect. However, their study did not examine models with binary instruments, vary rarity of treatment or outcome from approximately 0.5, examine alternative forms of 2SRI residuals, or report coverage probabilities of estimates.…”
Section: Discussionsupporting
confidence: 83%
“…However, 2SRI estimates of ATE were also generally biased, with the level of bias varying by residual form and outcome rarity. General conclusions from results of these simulation models are consistent with those of the more limited scenarios considered by Chapman and Brooks (). Among 2SRI models, those using generalized residuals were most often least biased in estimating ATE, though 2SRI with Anscombe residuals generated less biased estimates in scenarios with very rare outcomes (<5%).…”
Section: Introductionsupporting
confidence: 84%
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“…Proper generalization of estimates of ATT or LATE for the outcome of interest to the untreated patients in a study population requires the assumption that treatments were not chosen based on expected treatment benefit, i.e. no ‘sorting on the gain’ or essential heterogeneity [18, 51]. It has also been shown that different IVs affect the treatment choices of a different subset of patients in the same study, producing different but valid estimates of LATE [1821].…”
Section: Discussionmentioning
confidence: 99%
“…69 Linear 2-stage least squares (2SLS) IV estimators were used (Data S4). 29,[70][71][72][73][74][75][76][77][78][79][80] In this study 2SLS yields estimates of the absolute average effect of ACEI/ARBs for the patients whose ACEI/ARB choice was sensitive to local area practice styles 71,80 or what is known as the local average treatment effect. Our large sample size ensures that our 2SLS estimates will be distributed normally via the central limit theorem.…”
Section: Discussionmentioning
confidence: 99%