Quasiexperiments

Quasiexperiment is a research design used to estimate causal effects of an intervention when random assignment to treatment and control groups is not possible. Researchers rely on naturally occurring or policy-driven variation that approximates randomization, but units may differ in ways that affect outcomes. Causal claims depend on design assumptions and careful handling of confounding factors.

Common designs include non-equivalent groups designs, regression discontinuity designs, interrupted time series, difference-in-differences, natural experiments, and stepped-wedge experiments. Each uses observed data to compare treated and untreated units, or to compare outcomes before and after an intervention, while attempting to rule out alternative explanations.

Quasiexperiments are valuable when true experiments are impractical or unethical, and can yield credible causal inferences under strong design and analysis. They remain vulnerable to selection bias, history, maturation, and other confounds. Validity improves with pretests, matching or propensity score methods, fixed effects, instrumental variables, and robust sensitivity analyses that test key assumptions.

Applications span economics, education, public health, and social policy, including program evaluations, policy changes, and natural experiments from real-world settings. Examples include evaluating a school reform implemented in some districts, assessing the effects of a new tax or minimum wage policy using nearby control regions, and analyzing the impact of health campaigns over time.

Quasiexperiments offer a practical path to causal inference when randomization is not feasible, but require transparent reporting of assumptions and limitations to support credible conclusions.