Applied Causal Inference Course for Data Scientists
Manisha Arora
Data Science Lead, Google
Banani Mohapatra
Data Science Manager, Walmart
Causal Inference for Real-World Decision Making
Most data science work stops at correlation, but business decisions require causation.
In practice, clean A/B tests are often not feasible. You’re working with observational data, confounding variables, and incomplete information, yet still expected to explain impact and guide decisions. Without a causal framework, analyses risk being misleading or inconclusive.
This workshop focuses on closing that gap. You will learn how to identify causal relationships using established methods, understand the assumptions behind them, and apply them correctly in real-world settings. Beyond techniques, the emphasis is on decision-making, when to trust results, when to be cautious, and how to choose the right approach.
The outcome is not just better analysis but also the ability to produce conclusions that are credible, defensible, and useful for decision-making.
What you’ll learn
Develop the analytical expertise to identify true cause-and-effect relationships and lead data-driven decision-making at the highest level.
Understand ground rules: SUTVA, interference, and why correlation ≠causation in business settings
Compare Rubin's Potential Outcomes framework with Pearl's DAG-based structural approach
Build intuition for counterfactual reasoning before applying any method
Use Difference-in-Differences to compare treatment vs. control trends over time
Exploit arbitrary cutoffs with Regression Discontinuity Design to estimate causal effects
Leverage natural randomness via Instrumental Variables to solve endogeneity
Match treated and control units on propensity scores to reduce confounding bias
Apply Inverse Probability Weighting and doubly robust AIPW estimators
Apply Inverse Probability Weighting and doubly robust AIPW estimators
Construct and interpret Directed Acyclic Graphs to encode and validate causal assumptions
Estimate subgroup-level effects using Meta-learners, T-learners, and related approaches
Apply DAG lab exercises to identify confounders, colliders, and valid adjustment sets
Learn directly from Manisha & Banani
Manisha Arora
Data Science Lead, Google | Founder, PrepVector | MIT, UT Austin & UCinc
Banani Mohapatra
Senior Manager, AI/ML & Data Science at Walmart | Generative AI,LLM | IIT Delhi
Who this course is for
Data scientists working with observational data who struggle to answer causal questions when A/B tests aren’t feasible.
Data scientists stepping into ownership roles who need a structured framework to choose and apply the right causal method.
Senior analysts and applied scientists responsible for making and defending high-impact, data-driven decisions.
What's included
Live sessions
Learn directly from Manisha Arora & Banani Mohapatra in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
9 lessons • 6 projects
Week 1
Day 1: Building the Foundations
Day 2: Advanced Methods
Schedule
Live sessions
8 hrs
Projects
4 hrs
Async content
4 hrs
Frequently asked questions
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