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Daniel Jacob

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Personal Details

First Name:Daniel
Middle Name:
Last Name:Jacob
Suffix:
RePEc Short-ID:pja608
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Affiliation

Wirtschaftswissenschaftliche Fakultät
Humboldt-Universität Berlin

Berlin, Germany
http://www.wiwi.hu-berlin.de/
RePEc:edi:wfhubde (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
  2. Daniel Jacob, 2021. "Variable Selection for Causal Inference via Outcome-Adaptive Random Forest," Papers 2109.04154, arXiv.org.
  3. Jacob, Daniel, 2020. "Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects," IRTG 1792 Discussion Papers 2020-014, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  4. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
  5. Haupt, Johannes & Jacob, Daniel & Gubela, Robin M. & Lessmann, Stefan, 2019. "Affordable Uplift: Supervised Randomization in Controlled Exprtiments," IRTG 1792 Discussion Papers 2019-026, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

Articles

  1. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.

    Cited by:

    1. Vinish Shrestha, 2024. "Heterogeneous Impacts of ACA-Medicaid Expansion on Insurance and Labor Market Outcomes in the American South," Working Papers 2024-08, Towson University, Department of Economics, revised Jun 2024.
    2. Olga Takács & János Vincze, 2023. "Heterogeneous wage structure effects: a partial European East-West comparison," CERS-IE WORKING PAPERS 2305, Institute of Economics, Centre for Economic and Regional Studies.
    3. Häusler, Konstantin & Xia, Hongyu, 2021. "Indices on cryptocurrencies: An evaluation," IRTG 1792 Discussion Papers 2021-014, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Kushal S. Shah & Haoda Fu & Michael R. Kosorok, 2023. "Stabilized direct learning for efficient estimation of individualized treatment rules," Biometrics, The International Biometric Society, vol. 79(4), pages 2843-2856, December.
    5. Aaron Baird & Yusen Xia, 2024. "Precision Digital Health," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(3), pages 261-271, June.
    6. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.

  2. Jacob, Daniel, 2020. "Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects," IRTG 1792 Discussion Papers 2020-014, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

    Cited by:

    1. Lu, Cuicui & Wang, Weining & Wooldridge, Jeffrey M., 2020. "Using generalized estimating equations to estimate nonlinear models with spatial data," IRTG 1792 Discussion Papers 2020-017, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    3. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
    4. Wang, Weining & Wooldridge, Jeffrey M. & Xu, Mengshan, 2020. "Improved Estimation of Dynamic Models of Conditional Means and Variances," IRTG 1792 Discussion Papers 2020-021, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. David Benatia, 2022. "Ring the alarm! Electricity markets, renewables, and the pandemic," Post-Print hal-03523180, HAL.
    6. Wang, Weining & Yu, Lining & Wang, Bingling, 2020. "Tail Event Driven Factor Augmented Dynamic Model," IRTG 1792 Discussion Papers 2020-022, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  3. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.

    Cited by:

    1. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, Institute of Labor Economics (IZA).
    2. Øystein Daljord & Carl F. Mela & Jason M. T. Roos & Jim Sprigg & Song Yao, 2023. "The Design and Targeting of Compliance Promotions," Marketing Science, INFORMS, vol. 42(5), pages 866-891, September.
    3. Jacob, Daniel & Härdle, Wolfgang Karl & Lessmann, Stefan, 2019. "Group Average Treatment Effects for Observational Studies," IRTG 1792 Discussion Papers 2019-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  4. Haupt, Johannes & Jacob, Daniel & Gubela, Robin M. & Lessmann, Stefan, 2019. "Affordable Uplift: Supervised Randomization in Controlled Exprtiments," IRTG 1792 Discussion Papers 2019-026, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

    Cited by:

    1. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.
    2. Jacob, Daniel & Härdle, Wolfgang Karl & Lessmann, Stefan, 2019. "Group Average Treatment Effects for Observational Studies," IRTG 1792 Discussion Papers 2019-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

Articles

  1. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.

    Cited by:

    1. Olga Takács & János Vincze, 2023. "Heterogeneous wage structure effects: a partial European East-West comparison," CERS-IE WORKING PAPERS 2305, Institute of Economics, Centre for Economic and Regional Studies.
    2. Häusler, Konstantin & Xia, Hongyu, 2021. "Indices on cryptocurrencies: An evaluation," IRTG 1792 Discussion Papers 2021-014, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 4 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (4) 2019-11-18 2021-03-08 2021-04-26 2021-09-13. Author is listed
  2. NEP-BIG: Big Data (3) 2019-11-18 2021-03-08 2021-04-26. Author is listed
  3. NEP-CMP: Computational Economics (3) 2019-11-18 2021-03-08 2021-04-26. Author is listed
  4. NEP-EXP: Experimental Economics (1) 2021-04-26. Author is listed
  5. NEP-ISF: Islamic Finance (1) 2021-09-13. Author is listed
  6. NEP-ORE: Operations Research (1) 2021-03-08. Author is listed

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