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.
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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.

This story was originally published on HackerNoon at: https://hackernoon.com/why-data-quality-is-becoming-a-core-developer-experience-metric.
Bad data secretly slows development. Learn why data quality APIs are becoming core DX infrastructure in API-first systems and how they accelerate teams.
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This story was written by: @melissaindia. Learn more about this writer by checking @melissaindia's about page, and for more stories, please visit hackernoon.com.

In API-first systems, poor data quality (invalid emails, duplicate records, etc.) creates unpredictable bugs, forces defensive coding, and makes releases feel risky. This "hidden tax" consumes time and mental energy that should go to building features. The fix? Treat data quality as core infrastructure. By using real-time validation APIs at the point of ingestion, you create predictable systems, simplify business logic, and build developer confidence. This turns a vicious cycle of complexity into a virtuous cycle of velocity and better architecture. Bottom line: Investing in data quality isn't just operational hygiene—it's a direct investment in your team's ability to ship faster and with more confidence.

This story was originally published on HackerNoon at: https://hackernoon.com/why-accuracy-fails-for-uplift-models-and-what-to-use-instead.
When it comes to uplift modeling, traditional performance metrics commonly used for other machine learning tasks may fall short.
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This story was written by: @eltsefon. Learn more about this writer by checking @eltsefon's about page, and for more stories, please visit hackernoon.com.

When it comes to uplift modeling, traditional performance metrics commonly used for other machine learning tasks may fall short.