How Random Experiments Reveal True Causal Effects in Education

Random Experiments

Gold Standard

Earlier we distinguished correlation from causation and described the conditions needed for a correlation to become a causal relationship. In the absence of bias, a correlation turns into causation; if the treatment and control groups are comparable except for the intervention, the observed association reflects a causal effect.

The first tool to eliminate bias is the random experiment . It randomly assigns individuals in a population to a treatment or control group, and the proportion receiving treatment need not be 50 % – even a 10 % treatment arm works.

Randomisation removes bias by making potential outcomes independent of the intervention.

In a randomised trial we do not want the observed outcome to be independent of the treatment; we want the potential outcomes – what would happen under treatment or control – to be independent of the assignment. This ensures the treatment is the only systematic difference between groups.

Online Learning

During the 2020 pandemic many institutions shifted to remote instruction. Researchers asked whether the shift affected student achievement. A naïve comparison of students in fully online schools versus traditional classrooms is problematic because of selection bias – online schools may attract more disciplined or less affluent students.

To obtain an unbiased estimate, the authors randomised class format: some students received face‑to‑face lectures, some only online, and some a blended mix. At the end of the term standardised test scores were collected.

With 323 observations the average score for face‑to‑face classes was 78.54, while online‑only classes averaged 73.63, a difference of –4.91 points. Thus, online instruction reduced average student scores by about five points in this experiment.

A sanity check examined pre‑treatment variables (gender, race, etc.) across groups. Most variables were balanced, though the black variable showed a slight imbalance, illustrating that even randomisation can produce small differences in small samples.

Ideal Experiment

Randomised controlled trials (RCTs) are the most reliable way to obtain causal effects and are required for drug approval in many countries. However, RCTs can be expensive, unethical, or infeasible in many settings (e.g., smoking during pregnancy, credit‑limit experiments, minimum‑wage studies).

When an ideal experiment is impossible, researchers should still ask: “If I could run the perfect experiment, what would it look like?” This mental exercise often reveals alternative strategies for causal identification.

Assignment Mechanism

In an RCT the mechanism that assigns units to treatment is random. Understanding the assignment mechanism is crucial for all causal inference methods, because it determines how confidently we can attribute observed differences to the treatment.

Purely observational data cannot reveal the assignment mechanism; domain knowledge is required to hypothesise plausible mechanisms and assess whether observed associations are genuine or spurious.

Key Takeaways

Random experiments provide the simplest and most convincing way to uncover causal effects by ensuring comparable treatment and control groups. While not always feasible, thinking about the ideal experiment helps guide the design of credible observational studies.