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{ "item_title" : "Applied Microeconometrics", "item_author" : [" Damian Clarke "], "item_description" : "A rigorous, cutting-edge overview of the range of methods used to conduct causal inference in the social sciences. This textbook provides a lucid, rigorous, and cutting-edge overview of the methods used to conduct causal inference in the social sciences, covering all the core techniques and latest advances. Offering a detailed survey of the current state of microeconometric theory, Damian Clarke delves deeply into machine learning applications and presents developments in difference-in-difference methods, instrumental variables, multiple hypothesis testing, and other advanced topics. With a diverse range of examples and exercises offering hands-on experience, Applied Microeconometrics equips graduate students and researchers to apply state-of-the art scholarship to actionable problems.Integrates a rich array of machine learning methods into causal modeling frameworksCovers recent advances in difference-in-differences and dynamic research designs, formal discussions of challenges related to inference and hypothesis testing, and heterogeneity analysisFeatures a breadth of real-world examples from recent papersIncludes coding implementation in Python, R, and Stata", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/0/26/205/364/0262053640_b.jpg", "price_data" : { "retail_price" : "80.00", "online_price" : "80.00", "our_price" : "80.00", "club_price" : "80.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Applied Microeconometrics|Damian Clarke

Applied Microeconometrics

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Overview

A rigorous, cutting-edge overview of the range of methods used to conduct causal inference in the social sciences. This textbook provides a lucid, rigorous, and cutting-edge overview of the methods used to conduct causal inference in the social sciences, covering all the core techniques and latest advances. Offering a detailed survey of the current state of microeconometric theory, Damian Clarke delves deeply into machine learning applications and presents developments in difference-in-difference methods, instrumental variables, multiple hypothesis testing, and other advanced topics. With a diverse range of examples and exercises offering hands-on experience, Applied Microeconometrics equips graduate students and researchers to apply state-of-the art scholarship to actionable problems.

  • Integrates a rich array of machine learning methods into causal modeling frameworks
  • Covers recent advances in difference-in-differences and dynamic research designs, formal discussions of challenges related to inference and hypothesis testing, and heterogeneity analysis
  • Features a breadth of real-world examples from recent papers
  • Includes coding implementation in Python, R, and Stata

This item is Non-Returnable

Details

  • ISBN-13: 9780262053648
  • ISBN-10: 0262053640
  • Publisher: MIT Press
  • Publish Date: June 2026
  • Dimensions: 8.91 x 8.01 x 1.11 inches
  • Shipping Weight: 1.8 pounds
  • Page Count: 384

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