Causal Data Science with Directed Acyclic Graphs
4.7 (113 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
586 students enrolled

Causal Data Science with Directed Acyclic Graphs

Get to know the modern tools for causal inference from machine learning and AI, with many practical examples in R
4.7 (113 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
586 students enrolled
Created by Paul Hünermund
Last updated 11/2019
English
English [Auto]
Current price: $13.99 Original price: $19.99 Discount: 30% off
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This course includes
  • 5 hours on-demand video
  • 12 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 27 lectures 04:57:30
+ Structural Causal Models, Interventions, and Graphs
6 lectures 01:00:35
Structural Causal Models
04:18
D-Separation
16:31
Interventions
12:31
R Examples
15:05
Appendix
06:49
+ Causal Discovery
5 lectures 34:52
Testable Implications of DAGs
04:19
R Interlude
02:36
Causal Discovery
05:58
The PC Algorithm
17:28
Practical Considerations
04:31
+ Confounding Bias and Surrogate Experiments
7 lectures 01:34:27
Confounding Bias
03:39
Backdoor Adjustment
10:20
Frontdoor Adjustment
03:53
Do-Calculus
15:24
R Examples 1
29:39
Z-Identification
15:12
R Examples 2
16:20
+ Recovering from Selection Bias
3 lectures 29:05
Selection Bias
05:47
Recovering from Selelection Bias
10:58
R Examples
12:20
+ Transportability of Causal Knowledge Across Domains
4 lectures 57:57
The Transportability Task
10:16
S-Admissibility and Do-Calculus
12:25
Mz-Transportability
08:41
R Examples
26:35
+ Outro
1 lecture 04:53
The Causal Data Science Process
04:53
Requirements
  • Basic knowledge of probability and statistcs
  • Basic programming skills would be an advantage
Description

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 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