Randomized Experiment and Potential Outcome Model
Dive deep into the importance of randomization
Randomized experiment or randomized control trial (RCT) is regarded as the gold standard to test causality in medicine, science, and social science. In the tech industry, RCT takes the form of an online platform experiment and is often called A/B testing.
An RCT takes a group of subjects and randomly assigns them to either a treatment group, which gets the policy treatment/intervention or a control group, which receives no treatment. in an RCT, the subjects are assigned to treatment and control by the flip of a coin. Thus, the treatment group is identical to the control group in every facet but one: the treatment group gets the treatment.
Potential Outcome Model
The fundamental framework to uncover the causal effect of treatment from an RCT is Rubin Causal Model (RCM) — also called the Potential Outcome Model.
Let i denote any individual in the experiment, and Dᵢ denote the treatment as a binary random variable:
In an RCT setup, Dᵢ divides the experiment subjects into either treatment or control group, irrespective how the treatment is assigned (randomly or not):
