📈 Causal Inference - Can We Trust What Happens After?

Overview

How do you know if your new business strategy actually worked or if the trend was going to happen anyway? In this article, I introduce the Difference-in-Differences (DiD) method, a powerful statistical technique for estimating causal impact from observational data, using a real case study from my work at Bukalapak.

We walk through the full pipeline: from identifying the problem with naive before-after comparisons, validating assumptions like parallel trends, to implementing DiD with Python regression models and even conducting a placebo test. I also showcase a custom DiD Python library that makes the process seamless and reproducible.

Read the full article on Medium and discover how we turn business questions into causal answers.

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