Toward Unique and Unbiased Causal Effect Estimation From Data With Hidden Variables

2022 ◽  
pp. 1-13
Author(s):  
Debo Cheng ◽  
Jiuyong Li ◽  
Lin Liu ◽  
Kui Yu ◽  
Thuc Duy Le ◽  
...  
Keyword(s):  
Causal Effect ◽  
Hidden Variables ◽  
Effect Estimation

scholarly journals Comparison of Methods for Handling Covariate Missingness in Propensity Score Estimation with a Binary Exposure

2019 ◽  
Author(s):  
Donna Coffman ◽  
Jiangxiu Zhou ◽  
Xizhen Cai
Keyword(s):  
Propensity Score ◽  
Multiple Imputation ◽  
Missing Values ◽  
Propensity Scores ◽  
Causal Effect ◽  
Nonparametric Approach ◽  
Split Method ◽  
Mean Imputation ◽  
Substantial Bias ◽  
Effect Estimation

Abstract Background Causal effect estimation with observational data is subject to bias due to confounding, which is often controlled for using propensity scores. One unresolved issue in propensity score estimation is how to handle missing values in covariates.Method Several approaches have been proposed for handling covariate missingness, including multiple imputation (MI), multiple imputation with missingness pattern (MIMP), and treatment mean imputation. However, there are other potentially useful approaches that have not been evaluated, including single imputation (SI) + prediction error (PE), SI+PE + parameter uncertainty (PU), and Generalized Boosted Modeling (GBM), which is a nonparametric approach for estimating propensity scores in which missing values are automatically handled in the estimation using a surrogate split method. To evaluate the performance of these approaches, a simulation study was conducted.Results Results suggested that SI+PE, SI+PE+PU, MI, and MIMP perform almost equally well and better than treatment mean imputation and GBM in terms of bias; however, MI and MIMP account for the additional uncertainty of imputing the missingness.Conclusions Applying GBM to the incomplete data and relying on the surrogate split approach resulted in substantial bias. Imputation prior to implementing GBM is recommended.

scholarly journals Causal Subclassification Tree Algorithm and Robust Causal Effect Estimation via Subclassification

2020 ◽  
Vol 10 (1) ◽  
pp. 40
Author(s):  
Tomoshige Nakamura ◽  
Mihoko Minami
Keyword(s):  
Propensity Score ◽  
Propensity Scores ◽  
Causal Effect ◽  
Model Misspecification ◽  
Robust Estimator ◽  
Propensity Score Model ◽  
Confounding Variables ◽  
Score Model ◽  
Effect Estimation ◽  
Weighting Methods

In observational studies, the existence of confounding variables should be attended to, and propensity score weighting methods are often used to eliminate their e ects. Although many causal estimators have been proposed based on propensity scores, these estimators generally assume that the propensity scores are properly estimated. However, researchers have found that even a slight misspecification of the propensity score model can result in a bias of estimated treatment effects. Model misspecification problems may occur in practice, and hence, using a robust estimator for causal effect is recommended. One such estimator is a subclassification estimator. Wang, Zhang, Richardson, & Zhou (2020) presented the conditions necessary for subclassification estimators to have $\sqrt{N}$-consistency and to be asymptotically well-defined and suggested an idea how to construct subclasses.

Do match officials give preferential treatment to the strongest football teams? An analysis of four top European clubs

2018 ◽  
Vol 14 (4) ◽  
pp. 185-199 ◽  
Author(s):  
Francesco Audrino
Keyword(s):  
Statistical Power ◽  
Causal Effect ◽  
Preferential Treatment ◽  
Data Set ◽  
Premier League ◽  
Football Clubs ◽  
European Football ◽  
Effect Estimation ◽  
English Premier League ◽  
General Opinion

Abstract We address the fiercely debated question of whether the strongest European football clubs get special, preferential treatment from match officials in their decisions on the teams’ players over the course of the teams’ trophy winning streaks. To give an empirical answer to this question, we apply a rigorous econometric analysis for causal effect estimation to a self-constructed data set. We consider the two clubs in the Italian Serie A that experienced a prolonged winning streak during the period 2006–2016, namely Internazionale Milan (Inter) and Juventus Turin, as well as one team from the German Bundesliga (Borussia Dortmund) and one from the English Premier League (Manchester United) that also experienced a winning streak during the same period. This allows us to perform an analysis with enough statistical power to be able to estimate properly the effect of interest. The general opinion among fans, sports journalists, and insiders that the strongest clubs are favored by match officials’ decisions is supported only by the results of the analysis we run for Juventus, whereas for the other clubs under investigation, we did not find any significant bias. During its winning streak, more yellow cards and total booking points (an aggregated measure of yellow and red cards) were given to Juventus opponents. These effects are not only statistically significant, but also have a sizeable impact.

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