Causal Inference for Real-World Decision Making

Hosted by Anirban Bhattacharyya

Mon, May 4, 2026

5:00 PM UTC (30 minutes)

Virtual (Zoom)

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What you'll learn

Choose the Right Causal Method for the Problem

Learn when to use DiD, IV, RDD, matching, or synthetic control for real business questions.

Validate Assumptions and Diagnose Failure Modes

Spot weak assumptions, run diagnostics, and identify failure modes before trusting results

Communicate Causal Results for Better Decisions

Present causal findings clearly, explain caveats, and make recommendations stakeholders can act on

Why this topic matters

Many of the most important real-world decisions can’t be tested with clean A/B experiments because experiments may be too costly, too slow, impractical, or ethically impossible. Causal inference helps us estimate true impact from observational data, avoid misleading conclusions from correlation alone, and make better product, business, policy, and operational decisions.

You'll learn from

Anirban Bhattacharyya

Seasoned data scientist with more than 10 years of experience in data science

Anirban is a Senior Data Scientist at Atlassian with more than 10 years of experience applying analytics, experimentation, and causal inference to real-world business problems. Over the course of his career, he has worked at companies including Google, Dropbox, Pinterest, eBay, and American Express, where he partnered with product, growth, and marketing teams to drive better decisions through data.


His work has focused on high-impact questions such as measuring the effect of product changes, understanding customer behavior, optimizing growth initiatives, and evaluating interventions when clean randomized experiments are not possible. Across these roles, he has developed deep expertise in causal inference, statistical modeling, experimentation, and product analytics, with a strong emphasis on making rigorous methods useful in messy, high-stakes business settings.


Anirban brings a practical, industry-centered perspective to teaching. Rather than focusing only on theory, he is passionate about helping students learn how to choose the right causal method, validate assumptions, diagnose failure modes, and communicate findings in a way that earns stakeholder trust and drives action.

Google
eBay
Pinterest
Dropbox
Atlassian

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