A Practical Guide to Difference-in-Differences — A 5-Part Series Summary
A quick recap of our 5-part series on Difference-in-Differences — from intuition to implementation and robustness checks. A practical guide for applying DiD to real-world product and policy decisions.
👋 Hey! This is Manisha Arora from PrepVector. Welcome to the Tech Growth Series, a newsletter that aims to bridge the gap between academic knowledge and practical aspects of data science. My goal is to simplify complicated data concepts, share my perspectives on the latest trends, and share my learnings from building and leading data teams.
Over the past few weeks, I collaborated with Banani to write a hands-on blog series demystifying one of the most widely used causal inference methods in applied data science: Difference-in-Differences (DiD).
From understanding the core intuition to applying the method on a real business scenario — and validating it with robustness checks — this series was designed for practitioners looking to go beyond theory and apply DiD to product and policy problems.
About the Authors:
Manisha Arora: Manisha is a Data Science Lead at Google Ads, where she leads the Measurement & Incrementality vertical across Search, YouTube, and Shopping. She has 12+ years of experience in enabling data-driven decision-making for product growth.
Banani Mohapatra: Banani is a seasoned data science product leader with over 12 years of experience across e-commerce, payments, and real estate domains. She currently leads a data science team for Walmart’s subscription product, driving growth while supporting fraud prevention and payment optimization. She is known for translating complex data into strategic solutions that accelerate business outcomes.
Here’s a quick summary of what we covered 👇
Part 1: Introduction to DiD
We started with the intuition: how DiD helps estimate causal effects by comparing outcome trends across treated and untreated groups — assuming parallel trends.
Part 2: How to Implement DiD
We walked through how to structure panel data, set up a fixed effects model, and interpret the key interaction term that captures the treatment effect.
Part 3A: Case Study – Measuring Free Shipping Impact
We applied DiD to a practical question: did introducing free shipping to a subset of users lead to measurable revenue lift?
Part 3B: Interpreting the Results
We translated the regression output into business impact: estimating incremental revenue and evaluating trade-offs for rollout decisions.
Part 4: Robustness Checks
Finally, we validated the findings by removing outliers, redefining control groups, varying time windows, and running diagnostic checks.
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Difference-in-Differences is a valuable tool for causal inference in non-experimental settings. But when it comes to driving product decisions, A/B testing remains the default. Yet, many teams fall short — running tests without clear hypotheses, business-aligned metrics, or actionable interpretation. Bridging this gap is key to turning experiments into strategy.
If you want to sharpen how you design, interpret, and communicate experiments, join our live workshop:
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Thanks for following the series — and a big shoutout to Banani for co-creating this with me.
Let me know what topics you'd like us to explore next!
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