ivmodel: An R Package for Inference and Sensitivity Analysis of Instrumental Variables Models with One Endogenous Variable

Summary A sensitivity analysis for an observational study assesses how much bias, due to nonrandom assignment of treatment, would be necessary to change the conclusions of an analysis that assumes treatment assignment was effectively random. The evidence for a treatment effect can be strengthened if two different analyses, which could be affected by different types of biases, are both somewhat insensitive to bias. The finding from the observational study is then said to be replicated. Evidence factors allow for two independent analyses to be constructed from the same dataset. When combining the evidence factors, the Type I error rate must be controlled to obtain valid inference. A powerful method is developed for controlling the familywise error rate for sensitivity analyses with evidence factors. It is shown that the Bahadur efficiency of sensitivity analysis for the combined evidence is greater than for either evidence factor alone. The proposed methods are illustrated through a study of the effect of radiation exposure on the risk of cancer. An R package, evidenceFactors, is available from CRAN to implement the methods of the paper.

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PARTIAL IDENTIFICATION OF NONSEPARABLE MODELS USING BINARY INSTRUMENTS

Econometric Theory ◽

10.1017/s0266466620000353 ◽

2020 ◽

pp. 1-32

Author(s):

Takuya Ishihara

Keyword(s):

Instrumental Variables ◽

Conditional Distribution ◽

Explanatory Variable ◽

Real Data ◽

Partial Identification ◽

Distribution Functions ◽

Endogenous Variable ◽

Structural Function ◽

Nonseparable Models ◽

Power Point

In this study, we explore the partial identification of nonseparable models with continuous endogenous and binary instrumental variables. We show that the structural function is partially identified when it is monotone or concave in the explanatory variable. D’Haultfœuille and Février (2015, Econometrica 83(3), 1199–1210) and Torgovitsky (2015, Econometrica 83(3), 1185–1197) prove the point identification of the structural function under a key assumption that the conditional distribution functions of the endogenous variable for different values of the instrumental variables have intersections. We demonstrate that, even if this assumption does not hold, monotonicity and concavity provide identification power. Point identification is achieved when the structural function is flat or linear with respect to the explanatory variable over a given interval. We compute the bounds using real data and show that our bounds are informative.

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SEMsens: An R Package for Sensitivity Analysis of Structural Equation Models With the Ant Colony Optimization Algorithm

Applied Psychological Measurement ◽

10.1177/01466216211063233 ◽

2022 ◽

pp. 014662162110632

Author(s):

Zuchao Shen ◽

Walter L. Leite

Keyword(s):

Sensitivity Analysis ◽

Ant Colony Optimization ◽

Optimization Algorithm ◽

Structural Equation ◽

Structural Equation Models ◽

R Package ◽

Ant Colony ◽

Ant Colony Optimization Algorithm

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ivporbit:An R package to estimate the instrumental variables probit model

The Journal of Open Source Software ◽

10.21105/joss.00523 ◽

2018 ◽

Vol 3 (22) ◽

pp. 523 ◽

Cited By ~ 1

Author(s):

Taha Zaghdoudi

Keyword(s):

Instrumental Variables ◽

Probit Model ◽

R Package

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An R package facilitating sensitivity analysis, calibration and forward simulations with the LPJ-GUESS dynamic vegetation model

Environmental Modelling & Software ◽

10.1016/j.envsoft.2018.09.004 ◽

2019 ◽

Vol 111 ◽

pp. 55-60 ◽

Cited By ~ 2

Author(s):

Maurizio Bagnara ◽

Ramiro Silveyra Gonzalez ◽

Stefan Reifenberg ◽

Jörg Steinkamp ◽

Thomas Hickler ◽

...

Keyword(s):

Sensitivity Analysis ◽

R Package ◽

Vegetation Model ◽

Dynamic Vegetation Model

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Pleiotropy-robust Mendelian Randomization

10.1101/072603 ◽

2016 ◽

Cited By ~ 3

Author(s):

Hans van Kippersluis ◽

Cornelius A Rietveld

Keyword(s):

Sensitivity Analysis ◽

Instrumental Variables ◽

Genetic Variants ◽

Large Scale ◽

Mendelian Randomization ◽

Causal Effect ◽

Pleiotropic Effects ◽

List Type ◽

Exclusion Restriction ◽

The Uk

AbstractBackgroundThe potential of Mendelian Randomization studies is rapidly expanding due to (i) the growing power of GWAS meta-analyses to detect genetic variants associated with several exposures, and (ii) the increasing availability of these genetic variants in large-scale surveys. However, without a proper biological understanding of the pleiotropic working of genetic variants, a fundamental assumption of Mendelian Randomization (the exclusion restriction) can always be contested.MethodsWe build upon and synthesize recent advances in the econometric literature on instrumental variables (IV) estimation that test and relax the exclusion restriction. Our Pleiotropy-robust Mendelian Randomization (PRMR) method first estimates the degree of pleiotropy, and in turn corrects for it. If a sample exists for which the genetic variants do not affect the exposure, and pleiotropic effects are homogenous, PRMR obtains unbiased estimates of causal effects in case of pleiotropy.ResultsSimulations show that existing MR methods produce biased estimators for realistic forms of pleiotropy. Under the aforementioned assumptions, PRMR produces unbiased estimators. We illustrate the practical use of PRMR by estimating the causal effect of (i) cigarettes smoked per day on Body Mass Index (BMI); (ii) prostate cancer on self-reported health, and (iii) educational attainment on BMI in the UK Biobank data.ConclusionsPRMR allows for instrumental variables that violate the exclusion restriction due to pleiotropy, and corrects for pleiotropy in the estimation of the causal effect. If the degree of pleiotropy is unknown, PRMR can still be used as a sensitivity analysis.Key messagesIf genetic variants have pleiotropic effects, causal estimates of Mendelian Randomization studies will be biased.Pleiotropy-robust Mendelian Randomization (PRMR) produces unbiased causal estimates in case (i) a subsample can be identified for which the genetic variants do not affect the exposure, and (ii) pleiotropic effects are homogenous.If such a subsample does not exist, PRMR can still routinely be reported as a sensitivity analysis in any MR analysis.If pleiotropic effects are not homogenous, PRMR can be used as an informal test to gauge the exclusion restriction.

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Identification of average effects under magnitude and sign restrictions on confounding

Quantitative Economics ◽

10.3982/qe689 ◽

2019 ◽

Vol 10 (4) ◽

pp. 1619-1657 ◽

Cited By ~ 2

Author(s):

Karim Chalak

Keyword(s):

Sensitivity Analysis ◽

Direct Effect ◽

Instrumental Variables ◽

Average Treatment Effect ◽

Structural Systems ◽

Omitted Variables ◽

Average Effect ◽

Omitted Variable Bias ◽

Marginal Effects ◽

Variable Bias

This paper studies measuring various average effects of X on Y in general structural systems with unobserved confounders U, a potential instrument Z, and a proxy W for U. We do not require X or Z to be exogenous given the covariates or W to be a perfect one‐to‐one mapping of U. We study the identification of coefficients in linear structures as well as covariate‐conditioned average nonparametric discrete and marginal effects (e.g., average treatment effect on the treated), and local and marginal treatment effects. First, we characterize the bias, due to the omitted variables U, of (nonparametric) regression and instrumental variables estimands, thereby generalizing the classic linear regression omitted variable bias formula. We then study the identification of the average effects of X on Y when U may statistically depend on X and Z. These average effects are point identified if the average direct effect of U on Y is zero, in which case exogeneity holds, or if W is a perfect proxy, in which case the ratio (contrast) of the average direct effect of U on Y to the average effect of U on W is also identified. More generally, restricting how the average direct effect of U on Y compares in magnitude and/or sign to the average effect of U on W can partially identify the average effects of X on Y. These restrictions on confounding are weaker than requiring benchmark assumptions, such as exogeneity or a perfect proxy, and enable a sensitivity analysis. After discussing estimation and inference, we apply this framework to study earnings equations.

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Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments

Political Analysis ◽

10.1093/pan/mps040 ◽

2013 ◽

Vol 21 (2) ◽

pp. 141-171 ◽

Cited By ~ 129

Author(s):

Kosuke Imai ◽

Teppei Yamamoto

Keyword(s):

Sensitivity Analysis ◽

Structural Equation ◽

Structural Equation Models ◽

Statistical Significance ◽

Media Framing ◽

R Package ◽

Social Scientists ◽

Mediation Effects ◽

Causal Mechanisms ◽

Causal Pathways

Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of the corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are causally independent of one another. In this article, we consider a set of alternative assumptions that are sufficient to identify the average causal mediation effects when multiple, causally related mediators exist. We develop a new sensitivity analysis for examining the robustness of empirical findings to the potential violation of a key identification assumption. We apply the proposed methods to three political psychology experiments, which examine alternative causal pathways between media framing and public opinion. Our analysis reveals that the validity of original conclusions is highly reliant on the assumed independence of alternative causal mechanisms, highlighting the importance of proposed sensitivity analysis. All of the proposed methods can be implemented via an open source R package, mediation.

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