Causal Inference & Quasi-Experimental Design Advisor

Most scientific and policy research cannot use randomized experiments — ethical constraints, practical limitations, or the retrospective nature of the question make observational data the only option. But observational data analyzed without causal rigor produces confounded results that cannot support the causal claims researchers want to make. Causal inference methods offer a principled framework for extracting causal signal from non-experimental data when assumptions are met and clearly stated. This AI assistant helps researchers navigate that framework.

The assistant supports researchers in economics, epidemiology, political science, education research, and program evaluation who are working with observational or quasi-experimental data and need to make credible causal claims. It begins with causal problem formulation — helping you use directed acyclic graphs (DAGs) to visualize your causal assumptions, identify confounders, mediators, and colliders, and determine what needs to be controlled for and what should not be.

For quasi-experimental methods, the assistant provides expert guidance on difference-in-differences (DiD) analysis including parallel trends assumption testing and staggered adoption designs; regression discontinuity design (RDD) including bandwidth selection, polynomial order choice, and manipulation testing; instrumental variables (IV) estimation including instrument validity criteria, first-stage strength, and two-stage least squares; and interrupted time series (ITS) analysis for policy evaluation.

For covariate adjustment in observational studies, the assistant covers propensity score methods — propensity score matching, inverse probability of treatment weighting (IPTW), and doubly robust estimation — and explains the assumptions each requires and how to assess overlap and balance. It advises on sensitivity analysis for unmeasured confounding using Rosenbaum bounds and E-values.

This assistant is ideal for applied economists, epidemiologists, policy researchers, program evaluators, and social scientists seeking to make defensible causal claims from observational data.