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Causal Inference for Data Science

In this course, you'll learn a framework for thinking about causality, and methods for estimating causal effects from observational data.
Who Is This For?

Data Professionals interested in building on their foundational statistics and Python knowledge to better understand and evaluate causal effects.

Any prerequisites?

Probability

  • Familiarity with topics such as random variables, probability distributions, expectation and variance, conditional probability.

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Inferential Statistics

  • Familiarity with topics such as Central Limit Theorem, hypothesis testing, confidence intervals, linear and logistic regression for statistical inference (eg., interpreting coefficients from a model).

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Python & Data Analysis

  • Familiarity with using Python as part of a Data Science workflow (Pandas, NumPy, Matplotlib, Seaborn).
  • Basic data cleaning and preparation skills.
What will I be able to do after this Course?

By the end of the course, you will know how to identify necessary assumptions, estimand of interest, and potential causes of bias for causal inference. You will estimate causal effect using propensity-score-based matching and weighting methods, identify when difference in differences is appropriate, and use it to estimate causal effects for observational data.

Reimbursement FAQ

Course Overview

Sprint 1: Introduction to Causal Inference
Learn how to identify the necessary assumptions, estimand of interest, and potential causes of bias for a causal inference problem.
Sprint 2: Matching and Weighting
Learn how to estimate a causal effect based on observational data using propensity-score-based matching and weighting methods.
Sprint 3: Difference in Differences
- Learn how to identify when difference in differences is appropriate and use it to estimate causal effects for observational data.
- Bring it all together in your Capstone Project, a real-world problem involving movie theater ticket sales. You'll analyze data on ticket sales for R-rated and censored versions of R-rated movies at two comparable theater chains and conduct a difference in differences analysis to estimate the effect of offering censored versions of R-rated films on ticket sales.

What’s in a Modal course?

1:1 Coaching
Receive personal guidance, instruction, and motivation from real, practicing industry experts.
Real-world simulations
Practical coursework blends simulated and real-world projects, ensuring you are building job-ready skills.
Integrated code editor
In-browser coding environment mitigates challenges while enabling paired programming and inline feedback.
Structure & flexibility
Engage with content when your schedule allows. Our assignments and deadlines help you stay on track and our coaches keep you accountable.
Individual guidance
Courses for a variety of career goals, skill needs, and company objectives, ensuring learning is both relevant and productive.
Capstones projects
Challenging and satisfying capstone projects allow you to demonstrate the skills you’ve learned, while reinforcing collaboration and business skills.

Meet our coaches

Linda Liu
Director, Data Science & Analytics

Working with the learners makes it an incredibly rewarding journey. The shared excitement and collaborative growth highlight the entire fulfilling coaching experience!

Nataliia Maksimova
Director, Business Intelligence

It's incredibly inspiring to introduce people to the fascinating world of data. Sharing my passion for data and showing that it's not just dry numbers but a creative field where you can grow and innovate is deeply rewarding.

Udit Mehrotra
Senior Data Scientist

Seeing the growth in my learners is not only heartening but also assuring because I know I had a significant role to play in shaping their journey.

Interested in buying multiple seats for your team?
Contact us