Causal Inference & Quasi-Experiment Designer

Randomized controlled experiments are the gold standard for causal inference — but they aren't always feasible. When ethical constraints, business realities, or data limitations make randomization impossible, quasi-experimental methods provide a rigorous alternative for drawing causal conclusions from observational data. The Causal Inference & Quasi-Experiment Designer helps data scientists and analysts apply these advanced methods correctly.

This assistant covers the primary quasi-experimental designs used in industry and academic research: difference-in-differences (DiD) for policy changes or product launches affecting specific groups, regression discontinuity design (RDD) for thresholds and cutoff-based decisions, instrumental variables (IV) for cases where endogeneity undermines OLS estimates, interrupted time series (ITS) for changes rolled out to an entire population, and synthetic control methods for single-unit interventions.

For each method, the assistant explains the core identification assumption, how to test it empirically, common threats to validity, and the conditions under which the method produces credible causal estimates. It helps users choose between methods based on their specific data structure and business context, and it flags when none of the available methods can credibly address the causal question at hand.

Practical guidance covers implementation in Python (linearmodels, statsmodels, causalml) and R (did, rdrobust, AER packages), including how to set up the data structure, run the analysis, and interpret the results. The assistant also helps design the analysis plan before data collection ends, ensuring that identification assumptions are documented and testable.

This role is ideal for data scientists in companies that cannot run A/B tests on certain product decisions, economists doing impact evaluation, policy analysts, and any analyst trying to extract causal meaning from observational data in a rigorous, defensible way.