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Causal Inference in Data Science

Published by Pearson

Beginner to intermediate content levelBeginner to intermediate

A computational introduction to causality and counterfactual reasoning with Python

This training provides an invaluable, hands-on guide to applying causal inference in the wild to solve real-world data science tasks. Using an end-to-end example, we will walk through the process of posing a causal hypothesis, modeling our beliefs with causal graphs, estimating causal effects with the doWhy library in Python, and finally evaluating the soundness of our results. Rather than taking an abstract and mathematical approach to these steps, the focus of this training will be on accessible computational methods to practically answer causal questions in the context of a data science workflow.

What you’ll learn and how you can apply it

  • Understand how to reason causally and why it is necessary for modern data science.
  • Use the doWhy library to build, estimate, and evaluate causal models.
  • Learn how to practically apply causal inference to real-world data science problems.

This live event is for you because...

  • You have taken an introductory data science course or statistics course but want to take the next step to understand the foundations of causal inference and how to effectively apply the theory to real-world problems.
  • You have heard about the power of causal reasoning, but do not know how to get started learning its basics or applying it to your own problems.
  • You are an aspiring data scientist looking to break into the field and need to learn the practical skills necessary for what you will encounter on the job.
  • You are a quantitative researcher interested in applying theory to real projects by taking a computational approach to causal inference.
  • You are a software engineer interested in leveraging analytics to augment your application development process.

Prerequisites

  • Experience with an object-oriented programming language, e.g., Python (all code demos during the training will be in Python)
  • Familiarity with basic probability and statistics (e.g. distributions and hypothesis testing).
  • A working knowledge of the scientific Python libraries (numpy, pandas and scikit-learn) is helpful but not required.

Course Set-up

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Identifying Causal Effects 50min

  • Randomized Control Trials
  • Counterfactuals and Potential Outcomes
  • Causal Graphical Models
  • Q&A

Estimating Causal Effects 50min

  • Propensity Score Matching
  • Instrument Variables
  • Causal Effect Inference with Machine Learning

Break 10 min

Evaluating Causal Models 30min

  • Random Confounders and Placebos
  • Cross Validation
  • Sensitivity Analysis
  • Q&A

Discovering Causal Structure 25min

  • Guess and Test
  • Automated Graph Discovery
  • Q&A

Your Instructor

Jonathan Dinu

Jonathan Dinu is a freelance data scientist and content creator. In addition to consulting with startups on the latest AI applications, he is writing a book on machine learning and generative AI for Pearson and creating educational content on his YouTube channel.

In addition to his professional data science experience, he has run data science trainings for a Fortune 100 company and taught workshops at Strata, PyData, & DataWeek (among others). He first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley, and in a former life he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop.

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Skill covered

Data Science