scholarly journals Educational inequalities in access to fixed prosthodontic treatment in Norway. Causal effects using the introduction of a school reform as an instrumental variable

2020 ◽  
Vol 260 ◽  
pp. 113105
Author(s):  
Jostein Grytten ◽  
Irene Skau
Keyword(s):  
School Reform ◽  
Instrumental Variable ◽  
Causal Effects ◽  
Educational Inequalities

scholarly journals Graphical Models for Quasi-experimental Designs

2015 ◽  
Vol 46 (2) ◽  
pp. 155-188 ◽  
Author(s):  
Peter M. Steiner ◽  
Yongnam Kim ◽  
Courtney E. Hall ◽  
Dan Su
Keyword(s):  
Graphical Models ◽  
Instrumental Variable ◽  
Regression Discontinuity ◽  
Experimental Designs ◽  
Causal Effects ◽  
Confounding Bias ◽  
Randomized Controlled ◽  
Quasi Experimental ◽  
Causal Graphs ◽  
Latent Subgroups

Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand and, thus, warrant a causal interpretation of the estimated effect. In this article, we discuss and compare the identifying assumptions of quasi-experiments using causal graphs. The increasing complexity of the causal graphs as one switches from an RCT to RD, IV, or PS designs reveals that the assumptions become stronger as the researcher’s control over treatment selection diminishes. We introduce limiting graphs for the RD design and conditional graphs for the latent subgroups of compliers, always takers, and never takers of the IV design, and argue that the PS is a collider that offsets confounding bias via collider bias.

Bayesian Versus Maximum Likelihood Estimation of Treatment Effects in Bivariate Probit Instrumental Variable Models

2018 ◽  
Vol 7 (3) ◽  
pp. 651-659 ◽  
Author(s):  
Florian M. Hollenbach ◽  
Jacob M. Montgomery ◽  
Adriana Crespo-Tenorio
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Instrumental Variable ◽  
Likelihood Estimation ◽  
Direct Calculation ◽  
Causal Effects ◽  
Parameter Estimates ◽  
Bivariate Probit ◽  
Probit Models ◽  
Quantities Of Interest

Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum likelihood approaches for estimating bivariate probit models are problematic. Numerical routines in popular software suites frequently generate inaccurate parameter estimates and even estimated correctly, maximum likelihood routines provide no straightforward way to produce estimates of uncertainty for causal quantities of interest. In this note, we show that adopting a Bayesian approach provides more accurate estimates of key parameters and facilitates the direct calculation of causal quantities along with their attendant measures of uncertainty.

scholarly journals Background Indicators

Econometrics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 20
Author(s):  
Burkhard Raunig
Keyword(s):  
Instrumental Variable ◽  
Latent Variable ◽  
Structural Model ◽  
Simulation Experiment ◽  
Random Noise ◽  
Empirical Work ◽  
Causal Effects ◽  
Economic Uncertainty ◽  
Aggregate Consumption ◽  
The Impact

It is customary to assume that an indicator of a latent variable is driven by the latent variable and some random noise. In contrast, a background indicator is also systematically influenced by variables outside the structural model of interest. Background indicators deserve attention because in empirical work they are difficult to distinguish from ordinary effect indicators. This paper assesses instrumental variable (IV) estimation of the effect of a latent variable in a linear model when a background indicator replaces the latent variable. It turns out that IV estimates are inconsistent in many important cases. In some cases, the estimates capture causal effects of the indicator rather than causal effects of the latent variable. A simulation experiment that considers the impact of economic uncertainty on aggregate consumption illustrates some of the results.

scholarly journals ENSINO MÉDIO BRASILEIRO: dualidade, diferenciação e desigualdade social

2019 ◽  
Vol 26 (4) ◽  
pp. 107
Author(s):  
Ronaldo Marcos de Lima Araújo
Keyword(s):  
High School ◽  
High Schools ◽  
School Reform ◽  
Secondary School ◽  
Public High Schools ◽  
Educational Inequalities ◽  
Brazilian Literature ◽  
Palabras Clave ◽  
School Differentiation ◽  
Additional Reference

O texto analisa a reforma em curso do ensino médio brasileiro. Problematiza a produção da área de trabalho e educação que, no Brasil, tem enfatizado o uso do conceito de dualidade para explicar o Ensino Médio; identifica a introdução desse conceito na literatura brasileira e suas influências teóricas; a partir do que defende a sua validade, porém também a sua insuficiência. Tomando o conceito de desigualdade como referência adicional conclui que a reforma tende a promover maior diferenciação escolar, hierarquizando as escolas e precarizando ainda mais a formação oferecida pelas escolas públicas de Ensino Médio das redes estaduais, aprofundando as desigualdades educacionais e embargando o futuro dos jovens pobres.Palavras-chave: Ensino Médio. Reforma do Ensino Médio. Dualidade educacional. Desigualdade. Diferenciação escolar.BRAZILIAN SECONDARY SCHOOL: dualism, difference and social inequalitiesAbstractAnalyses the ongoing reform of Brazilian high school considering the increase in school differentiation that it tends to produce. Problematizes the production of the area of work and education, has emphasized the use of the concept of duality to explain the Secondary School; identifies the introduction of this concept in Brazilian literature and its theoretical influences; from what it defends its validity, but also its insufficiency. Also taking the concept of inequality as an additional reference concludes that the reform tends to deepen the educational inequalities, hierarchizing the schools and precarious even more the training offered by public high schools of the state networks, making it difficult to the future of poor young people.Keywords: Secondary School. High School Reform. Educational Duality. Inequality. School Differentiation.ESCUELA SECUNDARIA BRASILEÑA: dualidad, diferenciación y desigualdad socialResumenEl texto analiza la reforma en curso de la escuela secundaria brasileña. Problematiza la producción del área de trabajo y la educación que, en Brasil, ha enfatizado el uso del concepto de dualidad para explicar la escuela secundaria; identifica la introducción de este concepto en la literatura brasileña y sus influencias teóricas; de lo que defiende su validez, pero también su insuficiencia. Tomando el concepto de desigualdad como referencia adicional, se concluye que la reforma tiende a promover una mayor diferenciación escolar, jerarquizar las escuelas y una mayor precariedad de la capacitación ofrecida por las escuelas públicas públicas, profundizar las desigualdades educativas y embargar el futuro de los jóvenes pobres. Palabras clave: Bachillerato. Reforma de la escuela secundaria. Dualidad educativa. Desigualdad Diferenciación escolar. 

scholarly journals Measurement errors in the binary instrumental variable model

Biometrika ◽  
2019 ◽  
Vol 107 (1) ◽  
pp. 238-245
Author(s):  
Zhichao Jiang ◽  
Peng Ding
Keyword(s):  
Measurement Error ◽  
Instrumental Variable ◽  
Measurement Errors ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Variable Model ◽  
Differential Measurement ◽  
True Value ◽  
Instrumental Variable Model ◽  
Outcome Biases

Summary Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumental variable, treatment and outcome. We first consider nondifferential measurement errors, that is, the mismeasured variable does not depend on other variables given its true value. We show that the measurement error of the instrumental variable does not bias the estimate, that the measurement error of the treatment biases the estimate away from zero, and that the measurement error of the outcome biases the estimate toward zero. Moreover, we derive sharp bounds on the causal effects without additional assumptions. These bounds are informative because they exclude zero. We then consider differential measurement errors, and focus on sensitivity analyses in those settings.

scholarly journals Child Protection and Child Outcomes: Measuring the Effects of Foster Care

2007 ◽  
Vol 97 (5) ◽  
pp. 1583-1610 ◽  
Author(s):  
Joseph J Doyle
Keyword(s):  
Foster Care ◽  
Juvenile Delinquency ◽  
Child Protection ◽  
Instrumental Variable ◽  
Child Outcomes ◽  
Causal Effects ◽  
Older Children ◽  
Teen Motherhood ◽  
Long Term Outcomes

Little is known about the effects of placing children who are abused or neglected into foster care. This paper uses the placement tendency of child protection investigators as an instrumental variable to identify causal effects of foster care on long-term outcomes—including juvenile delinquency, teen motherhood, and employment—among children in Illinois where a rotational assignment process effectively randomizes families to investigators. Large marginal treatment effect estimates suggest caution in the interpretation, but the results suggest that children on the margin of placement tend to have better outcomes when they remain at home, especially older children. (JEL H75, I38, J13)

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