scholarly journals Simple Tests for Selection Bias: Learning More from Instrumental Variables

2015 ◽  
Author(s):  
Dan Black ◽  
Joonhwi Joo ◽  
Robert J. LaLonde ◽  
Jeffrey Andrew Smith ◽  
Evan Taylor
Keyword(s):  
Selection Bias ◽  
Instrumental Variables ◽  
Bias Learning

The Promise of Peacekeeping: Protecting Civilians in Civil Wars

2020 ◽  
Vol 74 (4) ◽  
pp. 810-832
Author(s):  
Allison Carnegie ◽  
Christoph Mikulaschek
Keyword(s):  
International Relations ◽  
United Nations ◽  
Selection Bias ◽  
Civil Wars ◽  
Instrumental Variables ◽  
Security Council ◽  
Civil Conflict ◽  
United Nations Security Council ◽  
Peacekeeping Missions ◽  
Identification Strategy

AbstractDo peacekeepers protect civilians in civil conflict? Securing civilian safety is a key objective of contemporary peacekeeping missions, yet whether these efforts actually make a difference on the ground is widely debated in large part because of intractable endogeneity concerns and selection bias. To overcome these issues, we use an instrumental variables design, leveraging exogenous variation in the rotation of African members of the United Nations Security Council and looking at its effects on African civil wars. We show that states that wield more power send more peacekeepers to their preferred locations, and that these peacekeepers in turn help to protect civilians. We thus demonstrate the robustness of many existing results to a plausible identification strategy and present a method that can also be applied to other diverse settings in international relations.

Mitigating Selection Bias: A Bayesian Approach to Two-stage Causal Modeling With Instrumental Variables for Nonnormal Missing Data

2020 ◽  
pp. 004912412091492
Author(s):  
Dingjing Shi ◽  
Xin Tong
Keyword(s):  
Missing Data ◽  
Selection Bias ◽  
Instrumental Variables ◽  
Bayesian Approach ◽  
Real Data ◽  
Average Treatment Effect ◽  
Causal Modeling ◽  
Continuous Treatment ◽  
Nonnormal Data ◽  
Two Stage

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).

In Reply to ‘No Obvious Impact of NSAIDs on Risk of Kidney Failure: Causal or Another Selection Bias’

2020 ◽  
Vol 76 (5) ◽  
pp. 742-743 ◽  
Author(s):  
Min Zhan ◽  
Rebecca M. Doerfler ◽  
Jeffrey C. Fink
Keyword(s):  
Selection Bias ◽  
Kidney Failure

Correction: “Influence of Selection Bias on the Test Decision – A Simulation Study”

2014 ◽  
Vol 53 (05) ◽  
pp. 343-343
Keyword(s):  
Selection Bias ◽  
Simulation Study ◽  
Error Rate ◽  
Type I Error ◽  
Block Size ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rate ◽  
Representation Error ◽  
Numeric Representation

We have to report marginal changes in the empirical type I error rates for the cut-offs 2/3 and 4/7 of Table 4, Table 5 and Table 6 of the paper “Influence of Selection Bias on the Test Decision – A Simulation Study” by M. Tamm, E. Cramer, L. N. Kennes, N. Heussen (Methods Inf Med 2012; 51: 138 –143). In a small number of cases the kind of representation of numeric values in SAS has resulted in wrong categorization due to a numeric representation error of differences. We corrected the simulation by using the round function of SAS in the calculation process with the same seeds as before. For Table 4 the value for the cut-off 2/3 changes from 0.180323 to 0.153494. For Table 5 the value for the cut-off 4/7 changes from 0.144729 to 0.139626 and the value for the cut-off 2/3 changes from 0.114885 to 0.101773. For Table 6 the value for the cut-off 4/7 changes from 0.125528 to 0.122144 and the value for the cut-off 2/3 changes from 0.099488 to 0.090828. The sentence on p. 141 “E.g. for block size 4 and q = 2/3 the type I error rate is 18% (Table 4).” has to be replaced by “E.g. for block size 4 and q = 2/3 the type I error rate is 15.3% (Table 4).”. There were only minor changes smaller than 0.03. These changes do not affect the interpretation of the results or our recommendations.

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