Direct and indirect effects of continuous treatments based on generalized propensity score weighting
      
      
        
      
          BP2-STS
        
      
      
      
      
      
      
      
      
      
      
      
      
        27
        
        
      
      
      
      
      
      
      
      
      
      
      
      
      
       
      
      
      
        
        English
        
        
        
          This paper proposes semi- and nonparametric methods for disentangling the total  causal effect of a continuous treatment on an outcome variable into its natural direct  effect and the indirect effect that operates through one or several intermediate  variables or mediators. Our approach is based on weighting observations by the  inverse of two versions of the generalized propensity score (GPS), namely the  conditional density of treatment either given observed covariates or given covariates  and the mediator. Our effect estimators are shown to be asymptotically normal when  the GPS is estimated by either a parametric or a nonparametric kernel-based method.  We also provide a simulation study and an application to the Job Corps program.
        
        
       
      
      
      
        
        
        
        
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        - Faculté des sciences économiques et sociales et du management
 
        
        
        
        
        
        
        
        
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                  Economics
                
              
            
          
        
 
        
        
        
          
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      Faculté SES
    
  
  
 
        
        
        
          
        
        
        
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  RERO DOC
  
    
      309416
    
  
            
 
            
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  RERO
  
    
      R008804523
    
  
            
 
            
          
         
        
        
        
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          https://folia.unifr.ch/unifr/documents/306658
        
 
      
     
   
  
  
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