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Double Machine Learning based Program Evaluation under Unconfoundedness

Citations

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Cited by:

  1. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
  2. Cai, Yunhao & Jing, Peng & Wang, Baihui & Jiang, Chengxi & Wang, Yuan, 2023. "How does “over-hype” lead to public misconceptions about autonomous vehicles? A new insight applying causal inference," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
  3. Zheng, Yi & Ren, He, 2024. "COVID-19 vaccination and housing payments," Journal of Housing Economics, Elsevier, vol. 64(C).
  4. Sallin, Aurelién, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Economics Working Paper Series 2109, University of St. Gallen, School of Economics and Political Science.
  5. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
  6. 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.
  7. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
  8. Yiyan Huang & Cheuk Hang Leung & Xing Yan & Qi Wu & Shumin Ma & Zhiri Yuan & Dongdong Wang & Zhixiang Huang, 2022. "Robust Causal Learning for the Estimation of Average Treatment Effects," Papers 2209.01805, arXiv.org.
  9. Paul Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Sep 2024.
  10. Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers 2106.10141, arXiv.org, revised May 2023.
  11. Oyenubi, Adeola & Kollamparambil, Umakrishnan, 2023. "Does noncompliance with COVID-19 regulations impact the depressive symptoms of others?," Economic Modelling, Elsevier, vol. 120(C).
  12. Federica Mascolo & Nora Bearth & Fabian Muny & Michael Lechner & Jana Mareckova, 2024. "The Heterogeneous Effects of Active Labour Market Policies in Switzerland," Papers 2410.23322, arXiv.org.
  13. Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023. "Criminal charges, risk assessment and violent recidivism in cases of domestic abuse," LSE Research Online Documents on Economics 121374, London School of Economics and Political Science, LSE Library.
  14. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
  15. Strittmatter, Anthony & Wunsch, Conny, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," Working papers 2021/05, Faculty of Business and Economics - University of Basel.
  16. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
  17. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
  18. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
  19. Yong Bian & Xiqian Wang & Qin Zhang, 2023. "How Does China's Household Portfolio Selection Vary with Financial Inclusion?," Papers 2311.01206, arXiv.org.
  20. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
  21. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
  22. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
  23. Kueck, Jannis & Luo, Ye & Spindler, Martin & Wang, Zigan, 2023. "Estimation and inference of treatment effects with L2-boosting in high-dimensional settings," Journal of Econometrics, Elsevier, vol. 234(2), pages 714-731.
  24. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2023. "Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods," Health Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 194-217, January.
  25. Tomoko Nagai & Takayuki Okuda & Tomoya Nakamura & Yuichiro Sato & Yusuke Sato & Kensaku Kinjo & Kengo Kawamura & Shin Kikuta & Naoto Kumano-go, 2024. "Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments," Papers 2411.01498, arXiv.org.
  26. Luyuan Song & Xiaojun Zhang, 2024. "Estimating the Individual Treatment Effect with Different Treatment Group Sizes," Mathematics, MDPI, vol. 12(8), pages 1-17, April.
  27. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  28. Bonev, Petyo & Matsumoto, Shigeru, 2022. "An empirical evaluation of environmental Alternative Dispute Resolution methods," Economics Working Paper Series 2208, University of St. Gallen, School of Economics and Political Science.
  29. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
  30. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2024. "The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting," Health Economics, John Wiley & Sons, Ltd., vol. 33(6), pages 1387-1411, June.
  31. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
  32. Anoop Kumar & Suresh Dodda & Navin Kamuni & Rajeev Kumar Arora, 2024. "Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets," Papers 2404.07225, arXiv.org.
  33. Aur'elien Sallin, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Papers 2110.08807, arXiv.org, revised Feb 2022.
  34. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
  35. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.
  36. Wang, Xiqian & Bian, Yong & Zhang, Qin, 2023. "The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods," Energy Economics, Elsevier, vol. 125(C).
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