Econometrics with Machine Learning

The Book's Motivation

Abstracts (preliminary, some modifications may be done to avoid overlaps, etc.):
Chapter 1, July 27 version: PDF
Chapter 2, July 29 version: PDF
Chapter 3,
Chapter 4, July 27 version: PDF
Chapter 5,
Chapter 6, June 30 version: PDF
Chapter 7, July 28 version: PDF
Chapter 8,
Chapter 9,
Chapter 10, July 24 version: PDF
Chapter 11, July 22 version: DOC
Chapter 12,
Chapter 13,

1. An indicative and informative title for the contributions, as soon as on board in order to avoid unnecessary overlaps between the chapters. DONE
2. Finalise the list of chapters and contributors: April 2021. DONE
3. Get a (2-3 pages) outline for each chapter: July (end of) 2021 (will then contact and deal with the publisher). UNDER WAY
4. First full draft for all chapters: January (end of) 2022.
5. Final version for all chapters: May 2022.
6. Expected publication: Fall 2022.

Chapter 1 — Felix Chan, Laszlo Matyas, and TBA: (Comprehensive Survey of) Model Selection in Linear Models
Chapter 2 — Ben Weiern Yeo, Mark Harris, Ranjodh Singh and Felix Chan : (Comprehensive Survey of) Model Selection in Non-linear Models
Chapter 3 — Esfandiar Maasoumi and Ali Habibnia: Big Data: Econometric Modelling and Machine Learning
Chapter 4 — William Crown: Machine Learning and Causality
Chapter 5 — Marcelo Cunha Medeiros, TBA: (Comprehensive survey of) Econometric Forecasting and Prediction
Chapter 6 — Qingliang Fan, Yu-Chin Hsu and Robert Lieli: (Comprehensive survey of) Policy Evaluation
Chapter 7 — Oliver Kiss and Gyorgy Ruzicska: Econometrics with Machine Learning Using Network Data
Chapter 8 — Samuele Centorrino, Jean-Pierre Florens and Jean-Michel Loubes: Fairness in Machine Learning and in Econometrics
Chapter 9 — Amit K Gandhi: Machine Learning for Instrumental Variable and Structural Estimation Problems
Chapter 10 — Ekaterina Seregina: Graphical Models and their Interactions with Machine Learning in the Context of Eonomics and Finance
Chapter 11 — Walter Sosa Escudero: Poverty, Inequality and Development Studies with Machine Learning
Chapter 12 — Jantje Sönksen: Machine Learning for Asset Pricing
Chapter 13 — Daniel Marable, TBA: Practical Issues on Machine Learning Algorithms from a Computational Perspective