22.2 The Gold Standard: Randomized Controlled Trials
Randomized Controlled Trials (RCTs) are the holy grail of causal inference. Their power comes from random assignment, which ensures that any differences between treatment and control groups arise only due to the intervention.
RCTs provide:
- Unbiased estimates of treatment effects
- Elimination of confounding factors on average (although covariate imbalance can occur, necessitating techniques like [Rerandomization] to achieve a “platinum standard” set by Tukey (1993))
An RCT consists of two groups:
- Treatment group: Receives the intervention (e.g., a new marketing campaign, drug, or financial incentive).
- Control group: Does not receive the intervention, serving as a baseline.
Subjects from the same population are randomly assigned to either group. This randomization ensures that any observed differences in outcomes are due solely to the treatment—not external factors.
However, RCTs are easier to conduct in hard sciences (e.g., medicine or physics), where treatments and environments can be tightly controlled. In social sciences, challenges arise because:
- Human behavior is unpredictable.
- Some treatments are impossible or unethical to introduce (e.g., assigning individuals to poverty).
- Real-world environments are difficult to control.
To address these challenges, social scientists use Quasi-Experimental Methods to approximate experimental conditions.
RCTs establish internal validity, meaning that the observed treatment effects are causally interpretable. Even though random assignment is not the same as ceteris paribus (holding everything else constant), it achieves a similar effect: on average, treatment and control groups should be comparable.