scholarly journals Nonparametric bounds on treatment effects with imperfect instruments

2021 ◽  
Author(s):  
Kyunghoon Ban ◽  
Désiré Kédagni
Keyword(s):  
Treatment Response ◽  
Treatment Effect ◽  
Latent Variables ◽  
Instrumental Variable ◽  
Longitudinal Survey ◽  
Average Treatment Effect ◽  
National Longitudinal Survey ◽  
Returns To Schooling ◽  
Nonparametric Bounds ◽  
Nonparametric Models

Abstract This paper extends the identification results in Nevo and Rosen (2012) to nonparametric models. We derive nonparametric bounds on the average treatment effect when an imperfect instrument is available. As in Nevo and Rosen (2012), we assume that the correlation between the imperfect instrument and the unobserved latent variables has the same sign as the correlation between the endogenous variable and the latent variables. We show that the monotone treatment selection and monotone instrumental variable restrictions, introduced by Manski and Pepper (2000, 2009), jointly imply this assumption. Moreover, we show how the monotone treatment response assumption can help tighten the bounds. The identified set can be written in the form of intersection bounds, which is more conducive to inference. We illustrate our methodology using the National Longitudinal Survey of Young Men data to estimate returns to schooling.

scholarly journals Instrumental variable estimation of truncated local average treatment effects

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249642
Author(s):  
Byeong Yeob Choi
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Propensity Scores ◽  
Real Data ◽  
Target Population ◽  
Average Treatment Effect ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Local Average Treatment Effects ◽  
Local Average

Instrumental variable (IV) analysis is used to address unmeasured confounding when comparing two nonrandomized treatment groups. The local average treatment effect (LATE) is a causal estimand that can be identified by an IV. The LATE approach is appealing because its identification relies on weaker assumptions than those in other IV approaches requiring a homogeneous treatment effect assumption. If the instrument is confounded by some covariates, then one can use a weighting estimator, for which the outcome and treatment are weighted by instrumental propensity scores. The weighting estimator for the LATE has a large variance when the IV is weak and the target population, i.e., the compliers, is relatively small. We propose a truncated LATE that can be estimated more reliably than the regular LATE in the presence of a weak IV. In our approach, subjects who contribute substantially to the weak IV are identified by their probabilities of being compliers, and they are removed based on a pre-specified threshold. We discuss interpretation of the proposed estimand and related inference method. Simulation and real data experiments demonstrate that the proposed truncated LATE can be estimated more precisely than the standard LATE.

scholarly journals COMPLEMENTARITY AND IDENTIFICATION

2016 ◽  
Vol 33 (5) ◽  
pp. 1154-1185
Author(s):  
Tate Twinam
Keyword(s):  
Treatment Response ◽  
Treatment Effect ◽  
Urban Spatial Structure ◽  
Independence Assumption ◽  
Nonparametric Bounds ◽  
Long Run ◽  
Shape Restrictions ◽  
Multiple Treatments ◽  
Effect Heterogeneity ◽  
Policy Complementarity

This paper examines the identification power of assumptions that formalize the notion of complementarity in the context of a nonparametric bounds analysis of treatment response. I extend the literature on partial identification via shape restrictions by exploiting cross-dimensional restrictions on treatment response when treatments are multidimensional; the assumption of supermodularity can strengthen bounds on average treatment effects in studies of policy complementarity. This restriction can be combined with a statistical independence assumption to derive improved bounds on treatment effect distributions, aiding in the evaluation of complex randomized controlled trials. Complementarities arising from treatment effect heterogeneity can be incorporated through supermodular instrumental variables to strengthen identification in studies with one or multiple treatments. An application examining the long-run impact of zoning on the evolution of urban spatial structure illustrates the value of the proposed identification methods.

Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable

2013 ◽  
Vol 21 (4) ◽  
pp. 492-506 ◽  
Author(s):  
Peter M. Aronow ◽  
Allison Carnegie
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Causal Effect ◽  
Average Treatment Effect ◽  
Treatment Variable ◽  
Estimation Strategy ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Receive Treatment ◽  
Local Average

Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, researchers may instead be interested in the ATE for the entire population of interest. In this article, we develop a simple reweighting method for estimating the ATE, shedding light on the identification challenge posed in moving from the LATE to the ATE. We apply our method to two published experiments in political science in which we demonstrate that the LATE has the potential to substantively differ from the ATE.

scholarly journals Lung clearance index to monitor treatment response in pulmonary exacerbations in preschool children with cystic fibrosis

Thorax ◽  
2018 ◽  
Vol 73 (5) ◽  
pp. 451-458 ◽  
Author(s):  
Jonathan H Rayment ◽  
Sanja Stanojevic ◽  
Stephanie D Davis ◽  
George Retsch-Bogart ◽  
Felix Ratjen
Keyword(s):  
Cystic Fibrosis ◽  
Preschool Children ◽  
Treatment Response ◽  
Antibiotic Treatment ◽  
Treatment Effect ◽  
Average Treatment Effect ◽  
Lung Clearance ◽  
Lung Clearance Index ◽  
Pulmonary Exacerbations ◽  
Relative Change

BackgroundAntibiotic treatment for pulmonary symptoms in preschool children with cystic fibrosis (CF) varies among clinicians. The lung clearance index (LCI) is sensitive to early CF lung disease, but its utility to monitor pulmonary exacerbations in young children has not been assessed.ObjectiveWe aim to (1) understand how LCI changes during lower respiratory tract symptoms relative to a recent clinically stable measurement, (2) determine whether LCI can identify antibiotic treatment response and (3) compare LCI changes to changes in spirometric indices.MethodsLCI and spirometry were measured at quarterly clinic visits over a 12-month period in preschool children with CF. Symptomatic visits were identified and classified as treated or untreated. Treatment response was estimated using propensity score matching methods.Results104 symptomatic visits were identified in 78 participants. LCI increased from baseline in both treated (mean relative change +23.8% (95% CI 16.2 to 31.4)) and untreated symptomatic visits (mean relative change +11.2% (95% CI 2.4 to 19.9)). A significant antibiotic treatment effect was observed when LCI was used as the outcome measure (average treatment effect −15.5% (95% CI −25.4 to −5.6)) but not for z-score FEV1.ConclusionLCI significantly deteriorated with pulmonary symptoms relative to baseline and improved with antibiotic treatment. These data suggest that LCI may have a role in the routine clinical care of preschool children with CF.

Estimation of local treatment effects under the binary instrumental variable model

Biometrika ◽  
2021 ◽  
Author(s):  
Linbo Wang ◽  
Yuexia Zhang ◽  
Thomas S Richardson ◽  
James M Robins
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Local Treatment ◽  
Real Data ◽  
Average Treatment Effect ◽  
Variable Model ◽  
Average Treatment ◽  
Local Average Treatment Effect ◽  
Local Average ◽  
Instrumental Variable Model

Abstract Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper, we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges for causal estimation with a binary outcome, and show that surprisingly, it can be more difficult than the case with a continuous outcome. We propose novel modelling and estimating procedures that improve upon existing proposals in terms of model congeniality, interpretability, robustness and efficiency. Our approach is illustrated via simulation studies and a real data analysis.

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