Udemy
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
Development
Web Development Data Science Mobile Development Programming Languages Game Development Database Design & Development Software Testing Software Engineering Development Tools No-Code Development
Business
Entrepreneurship Communications Management Sales Business Strategy Operations Project Management Business Law Business Analytics & Intelligence Human Resources Industry E-Commerce Media Real Estate Other Business
Finance & Accounting
Accounting & Bookkeeping Compliance Cryptocurrency & Blockchain Economics Finance Finance Cert & Exam Prep Financial Modeling & Analysis Investing & Trading Money Management Tools Taxes Other Finance & Accounting
IT & Software
IT Certification Network & Security Hardware Operating Systems Other IT & Software
Office Productivity
Microsoft Apple Google SAP Oracle Other Office Productivity
Personal Development
Personal Transformation Personal Productivity Leadership Career Development Parenting & Relationships Happiness Esoteric Practices Religion & Spirituality Personal Brand Building Creativity Influence Self Esteem & Confidence Stress Management Memory & Study Skills Motivation Other Personal Development
Design
Web Design Graphic Design & Illustration Design Tools User Experience Design Game Design Design Thinking 3D & Animation Fashion Design Architectural Design Interior Design Other Design
Marketing
Digital Marketing Search Engine Optimization Social Media Marketing Branding Marketing Fundamentals Marketing Analytics & Automation Public Relations Advertising Video & Mobile Marketing Content Marketing Growth Hacking Affiliate Marketing Product Marketing Other Marketing
Lifestyle
Arts & Crafts Beauty & Makeup Esoteric Practices Food & Beverage Gaming Home Improvement Pet Care & Training Travel Other Lifestyle
Photography & Video
Digital Photography Photography Portrait Photography Photography Tools Commercial Photography Video Design Other Photography & Video
Health & Fitness
Fitness General Health Sports Nutrition Yoga Mental Health Dieting Self Defense Safety & First Aid Dance Meditation Other Health & Fitness
Music
Instruments Music Production Music Fundamentals Vocal Music Techniques Music Software Other Music
Teaching & Academics
Engineering Humanities Math Science Online Education Social Science Language Teacher Training Test Prep Other Teaching & Academics
AWS Certification Microsoft Certification AWS Certified Solutions Architect - Associate AWS Certified Cloud Practitioner CompTIA A+ Cisco CCNA CompTIA Security+ Amazon AWS AWS Certified Developer - Associate
Graphic Design Photoshop Adobe Illustrator Drawing Digital Painting InDesign Character Design Canva Figure Drawing
Life Coach Training Neuro-Linguistic Programming Mindfulness Personal Development Personal Transformation Life Purpose Meditation CBT Emotional Intelligence
Web Development JavaScript React CSS Angular PHP WordPress Node.Js Python
Google Flutter Android Development iOS Development Swift React Native Dart Programming Language Mobile Development Kotlin SwiftUI
Digital Marketing Google Ads (Adwords) Social Media Marketing Marketing Strategy Google Ads (AdWords) Certification Internet Marketing YouTube Marketing Email Marketing Retargeting
SQL Microsoft Power BI Tableau Business Analysis Business Intelligence MySQL Data Analysis Data Modeling Big Data
Business Fundamentals Entrepreneurship Fundamentals Online Business Business Strategy Business Plan Startup Freelancing Blogging Home Business
Unity Game Development Fundamentals Unreal Engine C# 3D Game Development C++ 2D Game Development Unreal Engine Blueprints Blender
30-Day Money-Back Guarantee
Development Data Science

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
Rating: 4.5 out of 54.5 (198 ratings)
1,037 students
Created by Paul Hünermund
Last updated 9/2020
English
English [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

7 sections • 27 lectures • 4h 57m total length

  • Preview15:41

  • Preview05:21
  • Structural Causal Models
    04:18
  • D-Separation
    16:31
  • Interventions
    12:31
  • R Examples
    15:05
  • Appendix
    06:49

  • 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
    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

  • Selection Bias
    05:47
  • Recovering from Selelection Bias
    10:58
  • R Examples
    12:20

  • The Transportability Task
    10:16
  • S-Admissibility and Do-Calculus
    12:25
  • Mz-Transportability
    08:41
  • R Examples
    26:35

  • 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 recently gained 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

Instructor

Paul Hünermund
Professor for Business Economics
Paul Hünermund
  • 4.5 Instructor Rating
  • 198 Reviews
  • 1,037 Students
  • 1 Course

I’m an assistant professor at Copenhagen Business School, department for Strategy & Innovation. I studied economics at the University of Mannheim, HEC Lausanne, and New York University, and obtained a PhD in business economics from KU Leuven, Belgium, in 2017. My research focusses on empirical studies in the area of innovation, strategy and entrepreneurship and has been published in leading outlets such as Harvard Business Review and Research Policy. Moreover, I gained practical experience in policy consulting for, among others, the European Commission and the German Federal Ministry of Education and Research.

  • Udemy for Business
  • Teach on Udemy
  • Get the app
  • About us
  • Contact us
  • Careers
  • Blog
  • Help and Support
  • Affiliate
  • Terms
  • Privacy policy
  • Cookie settings
  • Sitemap
  • Featured courses
Udemy
© 2021 Udemy, Inc.