Mitigating Selection Bias

This study proposes a two-stage causal modeling with instrumental variables to mitigate selection bias, provide correct standard error estimates, and address nonnormal and missing data issues simultaneously. Bayesian methods are used for model estimation. Robust methods with Student’s t distributions are used to account for nonnormal data. Ignorable missing data are handled by multiple imputation techniques, while nonignorable missing data are handled by an added-on selection model structure. In addition to categorical treatment data, this study extends the work to continuous treatment variables. Monte Carlo simulation studies are conducted showing that the proposed Bayesian approach can well address common issues in existing methods. We provide a real data example on the early childhood relative age effect study to illustrate the application of the proposed method. The proposed method can be easily implemented using the R software package "ALMOND" (Analysis of Local Average Treatment Effect for missing or/and Nonnormal Data)

Bayesian probability · Categorical variable · Data mining · Econometrics · Imputation (statistics · Instrumental variable · Machine learning · Missing data · Model selection · Selection bias · Statistics · Advanced Causal Inference Techniques · Artificial Intelligence · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference

• The relationship between geographic distance to environmental protection agencies and industrial pollution emissions

  Daqian Shi, Kai Hu•Scottish Geographical Journal•2025

  Incluir item

• Impacts of interlocked contractual arrangements on dairy farmers’ welfare in Zambia

  Rebecca N Kiwanuka-Lubinda, John N Ng’ombe et al.•Agrekon•2021

  Incluir item

• Statistical analysis with missing data

  Roderick J A Little•Statistical analysis with missing…•2002

  Incluir item

• Analysis of Incomplete Multivariate Data

  Joseph L Schafer•Analysis of Incomplete…•1997

  Incluir item

• Robust Statistics

  Open Access•Peter J Huber•Robust statistics•1981

  Incluir item

• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

  Open Access•Stuart Geman, Donald Geman•IEEE Transactions on Pattern…•1984

  Incluir item

• The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data.

  Craig K Enders•Psychological Methods•2001

  Incluir item

• Modeling the Drop-Out Mechanism in Repeated-Measures Studies

  Roderick J A Little, Roderick J Little•Journal of the American…•1995

  Incluir item

• On the Joys of Missing Data

  Todd D Little, Terrence D Jorgensen et al.•Journal of Pediatric Psychology•2014

  Incluir item

• How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power

  Linda K Muthén, Bengt Muthén et al.•Structural Equation Modeling: A…•2002

  Incluir item

• Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak

  John Bound, David A Jaeger et al.•Journal of the American…•1995

  Incluir item

• Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity

  Joshua D Angrist, Guido W Imbens•Journal of the American…•1995

  Incluir item

• Analyzing longitudinal data with missing values.

  Craig K Enders•Rehabilitation Psychology•2011

  Incluir item

• Missing Data Techniques for Structural Equation Modeling.

  P D Allison•Journal of Abnormal Psychology•2003

  Incluir item

• Average causal effects from nonrandomized studies

  Joseph L Schafer, Joseph Kang•Psychological Methods•2008

  Incluir item

• The Impact of Education on Fertility and Child Mortality

  Lucia Breierova, Esther Duflo•National Bureau of Economic…•2004

  Incluir item

• Are public housing projects good for kids?

  Open Access•Janet Currie, Janet M Currie et al.•Journal of Public Economics•2000

  Incluir item

• Treatments of Missing Data

  Michael Gold, Michael Steven Gold et al.•Structural Equation Modeling: A…•2000

  Incluir item

• Missing Data

  Open Access•Daniel A Newman•Organizational Research Methods•2014

  Incluir item

• Missing Data in Educational Research

  Open Access•James L Peugh, Craig K Enders•Review of Educational Research•2004

  Incluir item

• Inference and missing data

  Donald B Rubin•Biometrika•1976

  Incluir item

• The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models

  Craig K Enders, Craig Enders et al.•Structural Equation Modeling: A…•2001

  Incluir item

• Identification of Causal Effects Using Instrumental Variables

  Joshua D Angrist, Guido W Imbens et al.•Journal of the American…•1996

  Incluir item

• Missing data

  Joseph L Schafer, John W Graham et al.•Psychological Methods•2002

  Incluir item

• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity

  Halbert White•Econometrica•1980

  Incluir item

• Instrumental Variables Regression with Weak Instruments

  Douglas O Staiger, Douglas Staiger et al.•Econometrica•1997

  Incluir item

• Bayesian Measures of Model Complexity and Fit

  Open Access•David Spiegelhalter, David J Spiegelhalter et al.•Journal of the Royal Statistical…•2002

  Incluir item

• Multiple Imputation for Nonresponse in Surveys

  Open Access•Donald B Rubin•Multiple Imputation for…•1987

  Incluir item

• Reexamining Criminal Behavior

  David B Mustard•The Review of Economics and…•2003

  Incluir item

• Bayesian Robust Two-stage Causal Modeling with Nonnormal Missing Data

  Dingjing Shi, Xin Tong•Multivariate Behavioral Research•2018

  Incluir item

• Outlying Observation Diagnostics in Growth Curve Modeling

  Xin Tong, Zhiyong Zhang•Multivariate Behavioral Research•2017

  Incluir item

• Local Influence and Robust Procedures for Mediation Analysis

  Jiyun Zu, Ke-Hai Yuan•Multivariate Behavioral Research•2010

  Incluir item

• The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation

  Open Access•Dingjing Shi, Xin Tong•SAGE Open•2017

  Incluir item

• Do financial incentives help low-performing schools attract and keep academically talented teachers? Evidence from California

  Open Access•Jennifer Steele, Jennifer L Steele et al.•Journal of Policy Analysis and…•2010

  Incluir item

• The timing of preventive services for women and children

  Susan L Ettner•American Journal of Public Health•1996

  Incluir item

• Latent Variable Models Under Misspecification

  Open Access•K A Bollen, James B Kirby et al.•Sociological Methods & Research•2007

  Incluir item

• Robustness Studies in Covariance Structure Modeling

  Open Access•Jeffrey Johannes Hoogland, Anne Boomsma•Sociological Methods & Research•1998

  Incluir item

• Economic Shocks and Civil Conflict

  Edward Miguel, Shanker Satyanath et al.•Journal of Political Economy•2004

  Incluir item

• A Note on the Influence of Outliers on Parametric and Nonparametric Tests

  Donald W Zimmerman•The Journal of General Psychology•1994

  Incluir item

• Three Likelihood-Based Methods for Mean and Covariance Structure Analysis with Nonnormal Missing Data

  Open Access•Ke-Hai Yuan, Peter M Bentler•Sociological Methodology•2000

  Incluir item

• Does Compulsory School Attendance Affect Schooling and Earnings

  Joshua D Angrist, Alan B Krueger•The Quarterly Journal of Economics•1991

  Incluir item

• Peer Influences on Aspirations

  Otis Dudley Duncan, Archibald O Haller et al.•American Journal of Sociology•1968

  Incluir item