One post tagged with "Allye"

Welcome to the world of Causal Inference.

If you work with data in a production environment, you've likely heard of Propensity Score Matching (PSM). You may have even implemented it using libraries like causalinference or DoWhy.

Writing the code isn't difficult. With the modern Python ecosystem, you can calculate propensity scores, perform matching, and estimate effects (ATE/ATT) with just a few lines of code.

But when asked, "Can we really trust these results?" can you confidently say "Yes"? If not, you lose credibility. Or, could you answer immediately without breaking a cold sweat if a Staff Data Scientist fired these sharp questions at you?

• "If you change the random seed, does the result flip from positive to negative?"
• "Are these matches actually similar? Are you forcing pairs?"
• "How does the conclusion change if you tighten the caliper slightly?"
• "What are the characteristics of the data that was excluded (trimmed)?"

In this blog, we will thoroughly rethink the "classical" method of PSM from the perspective of modern production data science. We will also dig deep into the philosophy and specifications of why Allye's PSM Widget is designed not just as a calculation tool, but as a "cockpit for protecting analysis quality."

This should serve as a "map of the field" for beginners and an "implementation answer key" for experts.