Causal Data Science with Directed Acyclic Graphs

Causal Data Science with Directed Acyclic Graphs, available at $59.99, has an average rating of 4.48, with 27 lectures, based on 493 reviews, and has 2948 subscribers.

You will learn about Causal inference in data science and machine learning How to work with directed acylic graphs (DAG) Newest developments in causal AI This course is ideal for individuals who are Data scientists or Economists or Computer Scientists or People intersted in machine learning It is particularly useful for Data scientists or Economists or Computer Scientists or People intersted in machine learning.

Enroll now: Causal Data Science with Directed Acyclic Graphs

Summary

Title: Causal Data Science with Directed Acyclic Graphs

Price: $59.99

Average Rating: 4.48

Number of Lectures: 27

Number of Published Lectures: 27

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: 19.99

Quality Status: approved

Status: Live

What You Will Learn

Causal inference in data science and machine learning

How to work with directed acylic graphs (DAG)

Newest developments in causal AI

Who Should Attend

Data scientists

Economists

Computer Scientists

People intersted in machine learning

Target Audiences

Data scientists

Economists

Computer Scientists

People intersted in machine learning

This course offers an introduction into causal data science with directed acyclic graphs (DAG). DAGs combine mathematical graph theory with statistical probability concepts and provide a powerful approach to causal reasoning. Originally developed in the computer science and artificial intelligence field, they recently gained increasing traction also in other scientific disciplines (such as machine learning, economics, finance, health sciences, and philosophy). DAGs allow to check the validity of causal statements based on intuitive graphical criteria, that do not require algebra. In addition, they open the possibility to completely automatize the causal inference task with the help of special identification algorithms. As an encompassing framework for causal thinking, DAGs are becoming an essential tool for everyone interested in data science and machine learning.

The course provides a good overview of the theoretical advances that have been made in causal data science during the last thirty year. The focus lies on practical applications of the theory and students will be put into the position to apply causal data science methods in their own work. Hands-on examples, using the statistical software R, will guide through the presented material. There are no particular prerequisites, but a good working knowledge in basic statistics and some programming skills are a benefit.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome

Chapter 2: Structural Causal Models, Interventions, and Graphs

Lecture 1: Directed Acyclic Graphs

Lecture 2: Structural Causal Models

Lecture 3: D-Separation

Lecture 4: Interventions

Lecture 5: R Examples

Lecture 6: Appendix

Chapter 3: Causal Discovery

Lecture 1: Testable Implications of DAGs

Lecture 2: R Interlude

Lecture 3: Causal Discovery

Lecture 4: The PC Algorithm

Lecture 5: Practical Considerations

Chapter 4: Confounding Bias and Surrogate Experiments

Lecture 1: Confounding Bias

Lecture 2: Backdoor Adjustment

Lecture 3: Frontdoor Adjustment

Lecture 4: Do-Calculus

Lecture 5: R Examples 1

Lecture 6: Z-Identification

Lecture 7: R Examples 2

Chapter 5: Recovering from Selection Bias

Lecture 1: Selection Bias

Lecture 2: Recovering from Selelection Bias

Lecture 3: R Examples

Chapter 6: Transportability of Causal Knowledge Across Domains

Lecture 1: The Transportability Task

Lecture 2: S-Admissibility and Do-Calculus

Lecture 3: Mz-Transportability

Lecture 4: R Examples

Chapter 7: Outro

Lecture 1: The Causal Data Science Process

Instructors

Paul Hünermund
Professor for Business Economics

Rating Distribution

1 stars: 4 votes

2 stars: 8 votes

3 stars: 40 votes

4 stars: 168 votes

5 stars: 273 votes

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