Machine Learning for Experiments in the Social Sciences - Mark H White,Jon Green
-35% koodilla BOOKS
Toimitus 22-28 arkipäivässä
30 päivän palautusoikeus
"Causal inference and machine learning are typically introduced in the social sciences separately as theoretically distinct methodological traditions. However, applications of machine learning in causal inference are increasingly prevalent. This Element provides theoretical and practical introductions to machine learning for social scientists interested in applying such methods to experimental data. We show ... Täydellinen kuvaus
Saatat myös pitää
TOP
If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All
Eliezer Yudkowsky, Nate Soares
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
How To Think About AI: A Guide For The Perplexed
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
Gödel, Escher, Bach: An Eternal Golden Braid
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
AI Engineering: Building Applications with Foundation Models
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
Python Crash Course: A Hands-On, Project-Based Introduction to Programming
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
The Rust Programming Language
Steve Klabnik, Carol Nichols, Chris Krycho
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
How to Talk to AI: (And How Not To)
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
The Pragmatic Programmer: journey to mastery, 20th Anniversary Edition, 2/e: your journey to mastery, 20th Anniversary Edition
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
Hackers. 25th Anniversary Edition: Heroes of the Computer Revolution
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
The Art of Game Design: A Book of Lenses
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
How Linux Works: What Every Superuser Should Know
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
Hands-On Large Language Models: Language Understanding and Generation
Maarten Grootendorst, Jay Alammar
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
Fundamentals of Software Architecture: A Modern Engineering Approach
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
Speak Data: Artists, Scientists, Thinkers, and Dreamers on How We Live Our Lives in Numbers
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
Slanted 47 Digital Tools: Slanted Magazine
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
Clean Architecture: A Craftsman's Guide to Software Structure and Design
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
World of Warcraft Chronicle, Volume 2
-35% koodilla BOOKS
Toimittajalla varastossa
TOP
Linux Basics for Hackers, 2nd Edition: Getting Started with Networking, Scripting, and Security in Kali
-35% koodilla BOOKS
Toimittajalla varastossa
Kuvaus
"Causal inference and machine learning are typically introduced in the social sciences separately as theoretically distinct methodological traditions. However, applications of machine learning in causal inference are increasingly prevalent. This Element provides theoretical and practical introductions to machine learning for social scientists interested in applying such methods to experimental data. We show how machine learning can be useful for conducting robust causal inference and provide a theoretical foundation researchers can use to understand and apply new methods in this rapidly developing field. We then demonstrate two specific methods - the prediction rule ensemble and the causal random forest - for characterizing treatment effect heterogeneity in survey experiments and testing the extent to which such heterogeneity is robust to out-of-sample prediction. We conclude by discussing the limitations and tradeoffs of such methods, while directing readers to additional related methods available on the Comprehensive R Archive Network (CRAN)"--
Lisätietoja
| Kirjoittaja | Mark H White, Jon Green |
|---|---|
| Julkaisija | Cambridge University Press |
| Julkaisuvuosi | 2023 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9781009168229 |