Nineteenth-century scientist Gregor Mendel's work provides a foundational example of a controlled experiment. To discover the laws of genetic inheritance, he systematically cross-bred different varieties of pea plants and then carefully observed and measured a specific set of seven characteristics in the offspring.

Google

Controlled experiments are often considered the 'gold standard' for identifying causal effects. This method establishes a causal link by creating two groups through random assignment: a treatment group that receives an intervention and a control group that does not. By ensuring all other influencing factors are held constant, the random assignment makes the two groups comparable. As a result, any observed average difference in outcomes between the groups can be attributed to the intervention.

Controlled Experiments as the 'Gold Standard' for Causality

To cite the ebook, use The CORE Econ Team 2023 The Economy 2.0: Microeconomics Open access e-text https://core-econ.org/the-economy/.

To cite a unit, use The CORE Econ Team 2023 The Economy 2.0: Microeconomics Open access e-text https://core-econ.org/the-economy/ Unit [unit number] (url).

CORE Econ - The Economy 2.0: Microeconomics

Confounders are variables that affect both the outcome and the factor we are trying to determine as the cause.  The confounders are what RCTs try to "disarm."

For example, when measuring the effect of Alcohol use (X) on Lung cancer (Y), Smoking (Z) may be a confounder because those who consume more alcohol also tend to smoke more. 

Confounders in Causal Inference

To illustrate a conventional experiment, consider a study in agriculture. Here, the variable X could be the amount of fertilizer applied to a crop. The experimenter would change this variable while keeping other factors, such as the amount of water and sunlight, constant. The resulting crop yield would be variable Y. By holding other factors steady, the experimenter can isolate the effect of the fertilizer on the crop's growth.

Learn Before

Related