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Automatic Debiased Machine Learning of Causal and Structural Effects

Citations

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Cited by:

  1. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Jun 2025.
  2. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
  3. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
  4. TELLO, Mario D., 2024. "Inversión Pública En Infraestructura Y Crecimiento Regional En Perú, 2005-2020: Un Análisis Basado En Técnicas De Aprendizaje Automático Causal," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 24(2), pages 195-222.
  5. Sören Blomquist & Jerry A. Hausman & Whitney K. Newey, 2023. "The Econometrics of Nonlinear Budget Sets," Annual Review of Economics, Annual Reviews, vol. 15(1), pages 287-306, September.
  6. Julius Schaper, 2025. "Residualised Treatment Intensity and the Estimation of Average Partial Effects," Papers 2502.10301, arXiv.org.
  7. Semenova, Vira, 2025. "Generalized Lee bounds," Journal of Econometrics, Elsevier, vol. 251(C).
  8. Newham, Melissa & Valente, Marica, 2024. "The cost of influence: How gifts to physicians shape prescriptions and drug costs," Journal of Health Economics, Elsevier, vol. 95(C).
  9. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "LASSO Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Jan 2026.
  10. Jing Kong, 2025. "Causal Inference in High-Dimensional Generalized Linear Models with Binary Outcomes," Papers 2510.16669, arXiv.org.
  11. Jin, Zequn & Sun, Jisheng, 2025. "Neyman-orthogonal moment for instrumental variable quantile regression model with high dimensional data," Economics Letters, Elsevier, vol. 253(C).
  12. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
  13. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2025. "Robust Semiparametric Inference for Bayesian Additive Regression Trees," Papers 2509.24634, arXiv.org, revised Oct 2025.
  14. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
  15. Soren Blomquist & Anil Kumar & Che-Yuan Liang & Whitney K. Newey, 2022. "Nonlinear Budget Set Regressions for the Random Utility Model," Working Papers 2219, Federal Reserve Bank of Dallas.
  16. Paul S. Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Dec 2024.
  17. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
  18. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects," Econometrica, Econometric Society, vol. 90(3), pages 967-1027, May.
  19. Yikun Zhang & Yen-Chi Chen, 2025. "Doubly Robust Inference on Causal Derivative Effects for Continuous Treatments," Papers 2501.06969, arXiv.org, revised Apr 2025.
  20. Soren Blomquist & Anil Kumar & Whitney K. Newey, 2024. "Panel Estimation of Taxable Income Elasticities with Heterogeneity and Endogenous Budget Sets," Papers 2501.00633, arXiv.org.
  21. Amandeep Singh & Ye Liu & Hema Yoganarasimhan, 2023. "Choice Models and Permutation Invariance: Demand Estimation in Differentiated Products Markets," Papers 2307.07090, arXiv.org, revised Feb 2024.
  22. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
  23. Amarendra Sharma, 2025. "P-CRE-DML: A Novel Approach for Causal Inference in Non-Linear Panel Data," Papers 2506.23297, arXiv.org.
  24. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
  25. Manu Navjeevan, 2023. "An Identification and Dimensionality Robust Test for Instrumental Variables Models," Papers 2311.14892, arXiv.org, revised Dec 2024.
  26. Juan Carlos Escanciano & Telmo P'erez-Izquierdo, 2023. "Automatic Debiased Estimation with Machine Learning-Generated Regressors," Papers 2301.10643, arXiv.org, revised May 2025.
  27. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
  28. Jikai Jin & Lester Mackey & Vasilis Syrgkanis, 2025. "It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation," Papers 2507.02275, arXiv.org, revised Nov 2025.
  29. Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2024. "Contamination Bias in Linear Regressions," American Economic Review, American Economic Association, vol. 114(12), pages 4015-4051, December.
  30. Jing Kong, 2025. "On the Asymptotics of the Minimax Linear Estimator," Papers 2510.16661, arXiv.org.
  31. Manu Navjeevan & Rodrigo Pinto & Andres Santos, 2023. "Identification and Estimation in a Class of Potential Outcomes Models," Papers 2310.05311, arXiv.org.
  32. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org, revised Mar 2025.
  33. 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.
  34. Fabian Muny, 2025. "Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies," Papers 2506.11960, arXiv.org.
  35. Geonwoo Kim & Suyong Song, 2024. "Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables," Papers 2408.14671, arXiv.org.
  36. Philipp Bach & Victor Chernozhukov & Carlos Cinelli & Lin Jia & Sven Klaassen & Nils Skotara & Martin Spindler, 2025. "Sensitivity Analysis for Causal ML: A Use Case at Booking.com," Papers 2510.09109, arXiv.org.
  37. Chenyu Hou, 2023. "Learning and Subjective Expectation Formation: A Recurrent Neural Network Approach," Discussion Papers dp23-13, Department of Economics, Simon Fraser University.
  38. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
  39. Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2021. "On Estimating Multiple Treatment Effects with Regression," Working Papers 2021-41, Princeton University. Economics Department..
  40. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
  41. Sven Klaassen & Jan Rabenseifner & Jannis Kueck & Philipp Bach, 2025. "Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators," Papers 2503.17290, arXiv.org, revised May 2025.
  42. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
  43. Zhang, Jeffrey & Li, Wei & Miao, Wang & Tchetgen Tchetgen, Eric, 2023. "Proximal causal inference without uniqueness assumptions," Statistics & Probability Letters, Elsevier, vol. 198(C).
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