Kicking Off the Causal Inference Series: Learn with Me!
Join us as we dive into the world of Causal Inference — from the basics to advanced concepts, one step at a time. Let’s learn, apply, and demystify causality together!
👋 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
One of the most common challenges I see in the data science community is making the leap from correlation to causation. Many data scientists come from diverse backgrounds—engineering, economics, statistics, physics, and even social sciences—yet causal inference often feels like a missing piece in their toolkit.
At the same time, causal inference use cases have exploded across the tech industry. Whether it’s measuring the impact of product features, optimizing marketing campaigns, understanding user behavior, or evaluating policy changes, companies are increasingly relying on causal methods to make high-stakes decisions.
Imagine you’re analyzing an A/B test for a new product feature, and you notice that users who engage with the feature spend more money. Did the feature actually drive higher spending, or were high-value users just more likely to engage? The real question to be answered is - “Did the feature CAUSE people to spend higher?”
But let’s be real—causal inference can feel daunting. It’s packed with counterfactual thinking, potential outcomes, structural causal models, and a long list of statistical techniques. That’s where this series comes in!
Why We’re Launching This Series
As part of the Learn with Me initiative I kicked off a few months ago, I aim to upskill 1,000 data professionals over the next quarter. We kicked off things with the SQL Challenge which was a HUGE success! It provided a platform for data professionals to upskill their programming skills in an engaging way! Next, Banani and I are launching a new series to help data professionals level up in causal inference.
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.
What you can expect:
This series is about applications—how these methods are actually used in tech.
Here’s what you can expect:
✅ Deep technical content—rigorous explanations of causal inference methods
✅ Code walkthroughs—hands-on implementation using Python and real datasets
✅ Visualizations—DAGs, counterfactual reasoning, and intuitive breakdowns
✅ Industry case studies—how companies use causal inference in product analytics, marketing, and business strategy
✅ Common pitfalls & practical guidance—what to watch out for when applying causal methods in real-world settings
If you’re looking for more than just theory—if you want to see how causal inference actually drives business impact—this series is for you.
What’s Coming Up?
In our next edition, we’ll start by understanding the basics of Causal Inference. We’ll break down two major frameworks - Rubin’s Potential Outcomes Model and Pearl’s Structural Causal Models - and discuss when to use each causal inference method. Stay tuned!
Upcoming Courses:
Master Product Sense and AB Testing, and learn to use statistical methods to drive product growth. I focus on inculcating a problem-solving mindset, and application of data-driven strategies, including A/B Testing, ML, and Causal Inference, to drive product growth.
AI/ML Projects for Data Professionals
Gain hands-on experience and build a portfolio of industry AI/ML projects. Scope ML Projects, get stakeholder buy-in, and execute the workflow from data exploration to model deployment. You will learn to use coding best practices to solve end-to-end AI/ML Projects to showcase to the employer or clients.
Not sure which course aligns with your goals? Send me a message on LinkedIn with your background and aspirations, and I'll help you find the best fit for your journey.