Results 1 to 10 of about 151,292 (252)
An introduction to instrumental variables for epidemiologists [PDF]
International Journal of Epidemiology, 2000 Instrumental-variable (IV) methods were invented over 70 years ago, but remain uncommon in epidemiology. Over the past decade or so, non-parametric versions of IV methods have appeared that connect IV methods to causal and measurement-error models important in epidemiological applications.
Sander Greenland, Greenland Sander
exaly +7 more sources
Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models [PDF]
Entropy, 2022 This paper investigates the problem of selecting instrumental variables relative to a target causal influence X→Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders.
Feng Xie +5 more
doaj +2 more sources
The Modified Instrumental Variable (MIV) for Endogenous Instrumental Variables
Journal of Statistical Theory and Applications (JSTA)This article introduces an estimator aimed at reducing the inconsistency of instrumental variable estimates when the instrument is not fully exogenous.
Nicolas Ronderos Pulido
doaj +2 more sources
Latent class instrumental variables and the monotonicity assumption [PDF]
Emerging Themes in Epidemiology, 2020 A key aspect of the article by Lousdal on instrumental variables was a discussion of the monotonicity assumption. However, there was no mention of the history of the development of this assumption.
Stuart G. Baker
doaj +2 more sources
Cointegration Tests Using Instrumental Variables
International Journal of Empirical Economics, 2022 This paper proposes new cointegration tests based on instrumental variable (IV) estimation. An important property of our tests is that the asymptotic distribution remains standard normal (or Chi-square) regardless of the number of regressors, differing ...
Junsoo Lee, Ali Yucel
doaj +1 more source
REndo: Internal Instrumental Variables to Address Endogeneity
Journal of Statistical Software, 2023 Endogeneity is a common problem in any causal analysis. It arises when the independence assumption between an explanatory variable and the error in a statistical model is violated.
Raluca Gui +3 more
doaj +1 more source
Incarceration and mortality in the United States
SSM: Population Health, 2021 The ongoing COVID-19 pandemic has spotlighted the role of America's overcrowded prisons as vectors of ill health, but robust analyses of the degree to which high rates of incarceration impact population-level health outcomes remain scarce. In this paper,
Elias Nosrati +5 more
doaj +1 more source
Causal inference with imperfect instrumental variables
Journal of Causal Inference, 2022 Instrumental variables allow for quantification of cause and effect relationships even in the absence of interventions. To achieve this, a number of causal assumptions must be met, the most important of which is the independence assumption, which states ...
Miklin Nikolai +3 more
doaj +1 more source
Algorithm for adaptive observation based on method of instrumental variables [PDF]
E3S Web of Conferences, 2023 When the input signal and the output value of the object of control cannot be measured accurately, the state vector is estimated. The instrumental variables (IVs) method is a commonly used parameter estimation method [1-10].
Nikolov Nikola +5 more
doaj +1 more source
Instrumental variable analysis [PDF]
Nephrology Dialysis Transplantation, 2012 The main advantage of the randomized controlled trial (RCT) is the random assignment of treatment that prevents selection by prognosis. Nevertheless, only few RCTs can be performed given their high cost and the difficulties in conducting such studies.
Stel, V. S. +3 more
openaire +4 more sources