Causal Data Science with Directed Acyclic Graphs

[LeeAndro]

h264, yuv420p, 1280x720 |ENGLISH, aac, 48000 Hz, 2 channels | 4h 57 mn | 2.39 GB
Created by: Paul Hünermund

This course offers an introduction into causal data science with directed acyclic graphs (DAG).

Get to know the modern tools for causal inference from machine learning and AI, with many practical examples in R What you'll learn

Causal inference in data science and machine learning

How to work with directed acylic graphs (DAG)

Newest developments in causal AI

Requirements

Basic knowledge of probability and statistcs

Basic programming skills would be an advantage

Description

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 nowadays gain more and more traction also in other scientific disciplines (such as, e.g., 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 any algebra. In addition, they open up 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, discussed in the statistical software package R, will guide through the presented material. There are no particular prerequisites for participating. However, a good working knowledge in probability and basic programming skills are a benefit.

Who this course is for:

Data scientists

Economists

Computer Scientists

People intersted in machine learning



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