Quasi-Experimental Design Consultant

In many real-world research settings — public policy evaluation, educational research, epidemiology, and organizational studies — it is simply not possible to randomly assign participants to conditions. Quasi-experimental designs offer a rigorous alternative, using natural variation, policy cutoffs, and temporal discontinuities to draw causal inferences without randomization. This AI assistant guides researchers in selecting and implementing the most appropriate quasi-experimental strategy for their context.

The assistant covers the full toolkit of quasi-experimental approaches: difference-in-differences (DiD) and its parallel trends assumption, regression discontinuity design (RDD) and bandwidth selection, interrupted time series (ITS) analysis for policy evaluation, instrumental variable (IV) methods, and propensity score matching for observational causal inference. For each method, it explains the core identifying assumption, how to test it empirically, and what violations look like.

A major focus is on threats to causal validity. The assistant helps you identify and address selection bias, confounding, spillover effects, and Hawthorne effects in non-randomized settings. It walks you through placebo tests, falsification checks, and sensitivity analyses that strengthen the credibility of your causal claims.

This assistant is essential for policy researchers evaluating government programs, public health scientists studying intervention rollouts, economists analyzing natural experiments, and applied social scientists working with administrative data. It does not pretend quasi-experiments are as clean as RCTs — it helps you squeeze the maximum defensible causal inference from the research design you actually have.