Causal Inference in Business: Real-World Impact
About This Video
Learn how data scientists apply causal inference modeling to uncover true cause-and-effect relationships behind business decisions. In this WiDS Upskill Workshop, Shreya Bhattacherjee, Staff Data Scientist at Walmart Global Tech, explains how to move from correlation to causation using real-world examples and proven econometric methods.
You’ll explore essential techniques like Difference-in-Differences (DiD) and Propensity Score Matching (PSM), understand when to use them, and discover how they power smarter decision-making across product, marketing, and policy analytics.
Whether you’re a student, analyst, or data professional, this session will help you strengthen your grasp of impact evaluation, A/B testing alternatives, and robust causal modeling frameworks — core skills for anyone working with data.
00:00 – Introduction
02:10 – What Is Causal Inference?
08:45 – Selection Bias & Confounding
15:30 – Difference-in-Differences Explained
28:00 – Propensity Score Matching
38:40 – Common Assumptions in Causal Models
47:10 – Business Applications
55:30 – Q&A & Career Advice
In This Video
Walmart Global Tech
Shreya Bhattacherjee is a data science and analytics professional with 8+ years of experience in driving business impact through data-driven solutions. She completed her PhD in Applied Economics from University of California, Riverside back in 2015 and since then has been a part of several esteemed organizations in the finance and retail domains. Her expertise lies in Causal Inference, Randomized Control/Trial Experiments, Time Series Forecasting and Machine Learning Modeling. She is currently leading initiatives for Walmart’s Retail Analytics Platform called Scintilla, where she uses her background in Statistics and core Machine Learning to translate insights from retail data into actionable business strategies.