Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

Daniel Jacob

Contact

E-mail:

daniel.jacob [at] hu-berlin.de

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CV (PDF)

Phone:

 

+49 30 2093-99468

Office:

Office hours:

Dorotheenstraße 1, Room 0.04

Upon agreement
Postal address:

IRTG 1792 "High Dimensional Nonstationary Time Series"
School of Business and Economics
Humboldt-Universität zu Berlin
Unter den Linden 6
10099 Berlin, Germany

Education

2018 - present PhD student in Information Systems and Statistics,
Humboldt-Universität zu Berlin
10/2019 - 02/2020 Visiting researcher, WISE, Xiamen University, China
2015 - 2018 Master of Science in Economics, Humboldt-Universität zu Berlin
 

Publications

Haupt, J. and Jacob, D. and Gubela, R. M. and Lessmann S. (2019). Affordable Uplift: Supervised Randomization in Controlled Experiments, Proceedings in International Conference in Information Systems

Working Paper

Jacob,D. (2021). CATE Meets ML: Conditional Average Treatment Effect and Machine Learning, IRTG 1792 Discussion Paper

 

Jacob,D. (2020). Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects, IRTG 1792 Discussion Paper

 

Jacob,D. (2019). Group Average Treatment Effects for Observational Studies, IRTG 1792 Discussion Paper

 

Work in Progress 

Does Tenure Make you Love Your Job? Heterogeneous Treatment Effects from Tenure on Job Satisfaction (joint work with Qingliang Fan) 

 

Scientific Talks

 

2021

  • Asian Meeting of the Econometric Society, 2021, Curtin University, (Malaysia)

 

2020

  • American Causal Inference Conference 2020, Texas (USA) (postponed until 2021)
  • Causal Machine Learning Workshop, Einstein Congress Centre, St. Gallen (Switzerland)

 

2019 

  • Workshop in Microeconometrics, WISE, Xiamen University, Xiamen (China)
  • AI and Data Science Workshop, National Cheng Kung University, Tainan (Taiwan)
  • Workshop in Empirical Economics, Universität Potsdam, Berlin (Germany)
  • Stat of ML Conference, Charles University, Prague (Czech Republic)

 

Research Interest

  • Causal inference in observational studies

  • Econometrics and machine learning 

  • Semi- and nonparametric modelling 

 

Teaching

  • Non- and Semiparametric Modelling (WS 20/21) (scheduled)
  • Seminar Applied Predictive Analytics (SoSe 2020) (moodle-link)
  • Seminar Applied Predictive Analytics (SoSe 2019) (moodle-link)