Quasiexperimental

Quasiexperimental design is a term in research methods describing studies that aim to evaluate the causal impact of an intervention without random assignment of units to treatment and control groups. Because randomization is often impractical or unethical in real-world settings, quasiexperiments rely on comparison groups and statistical techniques to approximate the counterfactual outcome.

Common approaches include non-equivalent control group designs, interrupted time series, regression discontinuity designs, difference-in-differences, propensity score matching, instrumental variable methods, and analyses based on natural experiments. These designs seek to isolate the effect of an intervention by adjusting for preexisting differences or exploiting external sources of exogenous variation.

Strengths include feasibility in applied settings and relevance to policy evaluation, while limitations center on threats to internal validity, such as selection bias and unmeasured confounding. Other risks include history, maturation, instrumentation, and regression to the mean. Robust inference requires careful design, sensitivity analyses, and robustness checks.

Applications span education, public health, social programs, and economics. Central to interpretation are internal validity—the credibility of causal claims within the study—and external validity, i.e., generalizability. Examples include evaluating a new school program implemented in some districts or using a sharp policy threshold in regression discontinuity analyses.