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Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants

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TLDR
In this article, an instrument is defined as a random nudge toward acceptance of a treatment that affects outcomes only to the extent that it affects acceptance of the treatment, i.e., it is used to extract bits of random treatment assignment from a setting that is quite biased in its treatment assignments.
Abstract
An instrument is a random nudge toward acceptance of a treatment that affects outcomes only to the extent that it affects acceptance of the treatment. Nonetheless, in settings in which treatment assignment is mostly deliberate and not random, there may exist some essentially random nudges to accept treatment, so that use of an instrument might extract bits of random treatment assignment from a setting that is otherwise quite biased in its treatment assignments. An instrument is weak if the random nudges barely influence treatment assignment or strong if the nudges are often decisive in influencing treatment assignment. Although ideally an ostensibly random instrument is perfectly random and not biased, it is not possible to be certain of this; thus a typical concern is that even the instrument might be biased to some degree. It is known from theoretical arguments that weak instruments are invariably sensitive to extremely small biases; for this reason, strong instruments are preferred. The strength of an ...

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Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction

TL;DR: In this paper, two world-renowned experts present statistical methods for studying causal in nature: what would happen to individuals, or to groups, if part of their environment were changed?
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Instrumental variable methods for causal inference

TL;DR: This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed to provide valid estimates of causaleffects and sensitivity analysis for those assumptions; methods of estimation of causal Effects using instrumental variables; and sources of instrumental variables in health studies.
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Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data

TL;DR: In this paper, the authors proposed a new weighting method that finds the weights of minimum variance that adjust or balance the empirical distribution of the observed covariates up to levels prespecified by the researcher.
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Anesthesia technique, mortality, and length of stay after hip fracture surgery.

TL;DR: Among adults in acute care hospitals in New York State undergoing hip repair, the use of regional anesthesia compared with general anesthesia was not associated with lower 30-day mortality but was associated with a modestly shorter length of stay.
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Association of Primary Care Physician Supply With Population Mortality in the United States, 2005-2015.

TL;DR: Greater primary care doctor supply was associated with lower mortality, but per capita supply decreased between 2005 and 2015, and programs to explicitly direct more resources to primary care physician supply may be important for population health.
References
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Journal ArticleDOI

Estimating causal effects of treatments in randomized and nonrandomized studies.

TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
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Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak

TL;DR: In this article, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation.
Journal Article

Identification of Causal effects Using Instrumental Variables

TL;DR: In this paper, a framework for causal inference in settings where assignment to a binary treatment is ignorable, but compliance with the assignment is not perfect so that the receipt of treatment is nonignorable.
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Identification of Causal Effects Using Instrumental Variables

TL;DR: It is shown that the instrumental variables (IV) estimand can be embedded within the Rubin Causal Model (RCM) and that under some simple and easily interpretable assumptions, the IV estimand is the average causal effect for a subgroup of units, the compliers.
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Does Compulsory School Attendance Affect Schooling and Earnings

TL;DR: This paper found that the season of birth is related to educational attainment and earnings, and that roughly 25 percent of potential dropouts remain in school because of compulsory schooling laws. But, they did not study the effect of compulsory attendance laws on educational attainment.
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