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regression_discontinuity_misuse
Errors arising from improper application of regression discontinuity designs, including incorrect functional form assumptions, inappropriate bandwidth selection, or failure to detect manipulation of the assignment variable near the cutoff. These errors can produce spurious treatment effects.
A study of a scholarship program uses a test score cutoff but fits a linear model to data that is actually curved, producing a false 'jump' at the cutoff that is really a modeling artifact.
A city evaluates a speed camera program by comparing accident rates just above and below the 30 mph speed limit used to determine camera placement. Researchers use a very narrow bandwidth of only a few observations near the cutoff, producing an unstable estimate that swings wildly with the inclusion or exclusion of a single data point, and falsely conclude the cameras have no effect.
A study assessing whether students who barely pass a remedial math course (cutoff: 60%) earn higher wages later in life fits a single straight regression line across a dataset where the true relationship curves sharply near the cutoff. The apparent 'jump' in wages at the threshold is largely an artifact of forcing a linear model onto nonlinear data, not evidence that the course itself raises earnings.
Binary (yes/no) questions an LLM must answer to identify this aspect:
Is a treatment or intervention assigned based on a cutoff value of a continuous variable?
Type: binaryIs the effect estimated by comparing groups just above and below the cutoff?
Type: binaryAre assumptions about the functional form near the cutoff (linearity, bandwidth) being validated?
Type: binaryCould manipulation of the running variable near the cutoff invalidate the design?
Type: binaryErrors arising from improper application of regression discontinuity designs, including incorrect functional form assumptions, inappropriate bandwidth selection, or failure to detect manipulation of the assignment variable near the cutoff. These errors can produce spurious treatment effects.
Regression discontinuity is seen as a quasi-experimental 'gold standard,' and its technical requirements are easily overlooked. The apparent rigor of the design provides false confidence.
Test sensitivity to bandwidth and functional form choices. Check for bunching at the cutoff that would indicate manipulation. Use robust estimation methods.
Education policy evaluation, electoral threshold studies, regulatory compliance analysis, and health economics.
Use these tools to detect, analyze, or train this aspect.