When A/B Tests Aren’t Possible, Causal Inference Can Still Measure Marketing Impact

This story was originally published on HackerNoon at: https://hackernoon.com/when-ab-tests-arent-possible-causal-inference-can-still-measure-marketing-impact. Learn how to measure marketing impact without A/B tests using causal inference, Diff-in-Diff, synthetic control, and GeoLift. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #ab-testing, #data-analytics, #data-analysis, #causal-inference, #ab-testing-alternatives, #geolift, #diff-in-diff, #causal-inference-marketing, and more. This story was written by: @radiokocmoc_l45iej08. Learn more about this writer by checking @radiokocmoc_l45iej08's about page, and for more stories, please visit hackernoon.com. In many real‑world settings, running a randomized experiment is simply impossible. We’ll walk through Diff‑in‑Diff, Synthetic Control, and Meta’s GeoLift. We show how to prep your data, and provide ready‑to‑run code.