Ekonometrika fani bilan tanishuv

Causality, Ceteris Paribus, and Experiments (cont’d)

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  we can distinguish between
  
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    experimental data: “created” in a laboratory experiment
    
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    non-experimental / observational data: researcher = passive collector of the data
    
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  a large part of econometrics deals with how to get “correct” results despite working with non-experimental data.
  

Causality & Econometrics Sum-Up:

Econometric tools cannot be used to find causal links; these have to be found in economic theory. Econometrics can help us quantify causal effects and/or verify their presence. The challenge in here consist in dealing with non-experimental data where ceteris paribus conditions cannot be established.

A Note on Randomized Experiments

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  sometimes, even in experiments related to natural sciences, it
  impossible to enforce ceteris paribus conditions
  
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    example (crop yields): assessing the effect of a new fertilizer on soybeans
    
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      ceteris paribus = ruling out other yield-affecting factors such as
      rainfall, quality of land, presence of parasites etc.
      
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      experimental design:
      
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        Choose several one-acre plots of land.
        
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        Apply different amounts of fertilizer to each plot.
        
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        Use statistical methods to measure the association between yields and
        fertilizer amounts.
        
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      drawback: some of yield-affecting factors are not fully observed →
      impossible to choose “identical” plots of land
      
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      solution: statistical procedures still work correctly, if fertilizer amounts are independent of the other factors1 – e.g., if we choose fertilizer amounts completely at random → hence randomized