Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R (Paperback)
暫譯: 因果分析:影響評估與因果機器學習在 R 中的應用(平裝本)

Huber, Martin

  • 出版商: MIT
  • 出版日期: 2023-08-01
  • 售價: $2,530
  • 貴賓價: 9.5$2,404
  • 語言: 英文
  • 頁數: 336
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0262545918
  • ISBN-13: 9780262545914
  • 相關分類: Machine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

A comprehensive and cutting-edge introduction to quantitative methods of causal analysis, including new trends in machine learning.

Reasoning about cause and effect--the consequence of doing one thing versus another--is an integral part of our lives as human beings. In an increasingly digital and data-driven economy, the importance of sophisticated causal analysis only deepens. Presenting the most important quantitative methods for evaluating causal effects, this textbook provides graduate students and researchers with a clear and comprehensive introduction to the causal analysis of empirical data. Martin Huber's accessible approach highlights the intuition and motivation behind various methods while also providing formal discussions of key concepts using statistical notation. Causal Analysis covers several methodological developments not covered in other texts, including new trends in machine learning, the evaluation of interaction or interference effects, and recent research designs such as bunching or kink designs.

 

  • Most complete and cutting-edge introduction to causal analysis, including causal machine learning
  • Clean presentation of rigorous material avoids extraneous detail and emphasizes conceptual analogies over statistical notation
  • Supplies a range of applications and practical examples using R

商品描述(中文翻譯)

全面且前沿的因果分析定量方法介紹,包括機器學習的新趨勢。

對因果關係的推理——做一件事與另一件事的後果——是我們作為人類生活中不可或缺的一部分。在日益數位化和數據驅動的經濟中,精緻的因果分析的重要性愈加深刻。本教科書介紹了評估因果效應的最重要定量方法,為研究生和研究人員提供了對實證數據因果分析的清晰且全面的介紹。馬丁·霍伯(Martin Huber)以易於理解的方式強調了各種方法背後的直覺和動機,同時也使用統計符號對關鍵概念進行正式討論。《因果分析》涵蓋了其他文本中未涉及的幾個方法論發展,包括機器學習的新趨勢、互動或干擾效應的評估,以及最近的研究設計,如聚集設計或折點設計。


  • 最完整且前沿的因果分析介紹,包括因果機器學習

  • 清晰的嚴謹材料呈現,避免多餘細節,強調概念類比而非統計符號

  • 提供使用 R 的各種應用和實際範例

作者簡介

Martin Huber is Professor of Applied Econometrics at the University of Fribourg, Switzerland, where his research comprises both methodological and applied contributions in the fields of causal analysis and policy evaluation, machine learning, statistics, econometrics, and empirical economics.

作者簡介(中文翻譯)

馬丁·胡伯(Martin Huber)是瑞士弗里堡大學(University of Fribourg)應用計量經濟學的教授,他的研究涵蓋因果分析與政策評估、機器學習、統計學、計量經濟學及實證經濟學等領域的理論方法與應用貢獻。