---
title: "Scientific control"
chunk: 2/4
source: "https://en.wikipedia.org/wiki/Scientific_control"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:45:31.111708+00:00"
instance: "kb-cron"
---

Negative controls are variables that meant to help when the study design is suspected to be invalid because of unmeasured confounders that are correlated with both the treatment and the outcome. Where there are only two possible outcomes, e.g. positive or negative, if the treatment group and the negative control (non-treatment group) both produce a negative result, it can be inferred that the treatment had no effect. If the treatment group and the negative control both produce a positive result, it can be inferred that a confounding variable is involved in the phenomenon under study, and the positive results are not solely due to the treatment.
In other examples, outcomes might be measured as lengths, times, percentages, and so forth. In the drug testing example, we could measure the percentage of patients cured. In this case, the treatment is inferred to have no effect when the treatment group and the negative control produce the same results. Some improvement is expected in the placebo group due to the placebo effect, and this result sets the baseline upon which the treatment must improve upon. Even if the treatment group shows improvement, it needs to be compared to the placebo group. If the groups show the same effect, then the treatment was not responsible for the improvement (because the same number of patients were cured in the absence of the treatment). The treatment is only effective if the treatment group shows more improvement than the placebo group.

=== Negative Control Exposure (NCE) ===

NCE is a variable that should not causally affect the outcome, but may suffer from the same confounding as the exposure-outcome relationship in question. A priori, there should be no statistical association between the NCE and the outcome. If an association is found, then it through the unmeasured confounder, and since the NCE and treatment share the same confounding mechanism, there is an alternative path, apart from the direct path from the treatment to the outcome. In that case, the study design is invalid.
For example, Yerushalmy used husband's smoking as an NCE. The exposure was maternal smoking; the outcomes were various birth factors, such as incidence of low birth weight, length of pregnancy, and neonatal mortality rates. It is assumed that husband's smoking share common confounders, such household health lifestyle with the pregnant woman's smoking, but it does not causally affect the fetus development. Nonetheless, Yerushalmy found a statistical association, And as a result, it casts doubt on the proposition that cigarette smoking causally interferes with intrauterine development of the fetus.

==== Differences Between Negative Control Exposures and Placebo ====
The term negative controls is used when the study is based on observations, while the Placebo should be used as a non-treatment in randomized control trials.

=== Negative Control Outcome (NCO) ===

Negative Control Outcomes are the more popular type of negative controls. NCO is a variable that is not causally affected by the treatment, but suspected to have a similar confounding mechanism as the treatment-outcome relationship. If the study design is valid, there should be no statistical association between the NCO and the treatment. Thus, an association between them suggest that the design is invalid.
For example, Jackson et al. used mortality from all causes outside of influenza season an NCO in a study examining influenza vaccine's effect on influenza-related deaths. A possible confounding mechanism is health status and lifestyle, such as the people who are more healthy in general also tend to take the influenza vaccine. Jackson et al. found that a preferential receipt of vaccine by relatively healthy seniors, and that differences in health status between vaccinated and unvaccinated groups leads to bias in estimates of influenza vaccine effectiveness. In a similar example, when discussing the impact of air pollutants on asthma hospital admissions, Sheppard et al. et al. used non-elderly appendicitis hospital admissions as NCO.

==== Formal Conditions ====
Given a treatment 
  
    
      
        A
      
    
    {\displaystyle A}
  
 and an outcome 
  
    
      
        Y
      
    
    {\displaystyle Y}
  
, in the presence of a set of control variables 
  
    
      
        X
      
    
    {\displaystyle X}
  
, and unmeasured confounder 
  
    
      
        U
      
    
    {\displaystyle U}
  
 for the 
  
    
      
        A
        −
        Y
      
    
    {\displaystyle A-Y}
  
 relationship. Shi et al. presented formal conditions for a negative control outcome 
  
    
      
        
          
            
              Y
              ~
            
          
        
      
    
    {\displaystyle {\tilde {Y}}}
  
,

Stable Unit Treatment Value Assumption (SUTVA): For both 
  
    
      
        
          Y
        
      
    
    {\displaystyle {Y}}
  
 and 
  
    
      
        
          
            
              Y
              ~
            
          
        
      
    
    {\displaystyle {\tilde {Y}}}
  
 with regard to 
  
    
      
        A
        =
        a
      
    
    {\displaystyle A=a}
  
.
Latent Exchangeability: 
  
    
      
        
          Y
          
            A
            =
            a
          
        
        ⊥
        A
        
          |
        
        
        X
        ,
        U
      
    
    {\displaystyle Y^{A=a}\perp A|\;X,U}
  
 Given 
  
    
      
        X
      
    
    {\displaystyle X}
  
 and 
  
    
      
        U
      
    
    {\displaystyle U}
  
, the potential outcome 
  
    
      
        
          Y
          
            A
            =
            a
          
        
      
    
    {\displaystyle Y^{A=a}}
  
 is independent of the treatment.
Irrelevancy: Ensures the irrelevancy of the treatment on the NCO.

  
    
      
        
          
            
              
                Y
                ~
              
            
          
          
            A
            =
            a
          
        
        =
        
          
            
              
                Y
                ~
              
            
          
          
            A
            =
            
              a
              ′
            
          
        
        =
        
          
            
              Y
              ~
            
          
        
        
          |
        
        
        U
        ,
        X
      
    
    {\displaystyle {\tilde {Y}}^{A=a}={\tilde {Y}}^{A=a'}={\tilde {Y}}|\;U,X}
  
: There is no causal effect of 
  
    
      
        A
      
    
    {\displaystyle A}
  
 on 
  
    
      
        
          
            
              Y
              ~
            
          
        
      
    
    {\displaystyle {\tilde {Y}}}
  
 given 
  
    
      
        X
      
    
    {\displaystyle X}
  
 and 
  
    
      
        U
      
    
    {\displaystyle U}
  
.