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Prof. Martin Huber is a chair of applied econometrics and policy evaluation at the University of Fribourg. He is also a researcher and a consultant in causal inference, machine learning, and policy analysis.
Articles 1–20. University of Fribourg - Cited by 3,483 - microeconometrics - treatment evaluation - empirical labor economics - empirical health economics - machine learning.
Martin Andreas Huber ist ein deutscher Politiker. Er ist seit der Landtagswahl 2013 Mitglied des Bayerischen Landtags und seit dem 6. Mai 2022 Generalsekretär seiner Partei.
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.
Nov 1, 2023 · Former Quantapore founder and CEO brings 20 years of proteomics and genomics industry experience to company developing a platform for comprehensively quantifying the proteome. Martin Huber,...
To understand the idea and goals of machine learning for causal analysis. To understand the intuition, advantages, and disadvantages of alternative methods. To be able to apply causal machine learning to real world data using the software “R” and its interface “R Studio”.
Hugo Bodory & Martin Huber & Michael Lechner, 2022. "The finite sample performance of instrumental variable-based estimators of the Local Average Treatment Effect when controlling for covariates," Papers 2212.07379, arXiv.org. Yu-Chin Hsu & Martin Huber & Ying-Ying Lee & Chu-An Liu, 2021.
