Debiased machine learning of conditional average treatment effects and other causal functions

Citations

Cited by:

1. Goller, Daniel & Diem, Andrea & Wolter, Stefan C., 2023. "Sitting next to a dropout: Academic success of students with more educated peers," Economics of Education Review, Elsevier, vol. 93(C).
   • Daniel Goller & Andrea Diem & Stefan C. Wolter, 2022. "Sitting Next to a Dropout - Academic Success of Students with More Educated Peers," CESifo Working Paper Series 9812, CESifo.
   • Goller, Daniel & Diem, Andrea & Wolter, Stefan C., 2022. "Sitting Next to a Dropout: Academic Success of Students with More Educated Peers," IZA Discussion Papers 15378, IZA Network @ LISER.
2. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
3. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
   • Martin Huber & Jonas Meier & Hannes Wallimann, 2021. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Papers 2105.01426, arXiv.org, revised Jun 2022.
4. Daniel Goller & Andrea Diem & Stefan C. Wolter, 2022. "Sitting next to a dropout: Study success of students with peers that came to the lecture hall by a different route," Economics of Education Working Paper Series 0190, University of Zurich, Department of Business Administration (IBW).
5. Phillip Heiler & Michael C. Knaus, 2025. "Heterogeneity Analysis with Heterogeneous Treatments," Papers 2507.01517, arXiv.org, revised Feb 2026.
6. Abdul-Nasah Soale & Emmanuel Selorm Tsyawo, 2023. "Clustered Covariate Regression," Papers 2302.09255, arXiv.org, revised Jul 2025.
7. 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.
   • Daniel Goller, 2020. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Papers 2008.07165, arXiv.org.
   • Goller, Daniel, 2020. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Economics Working Paper Series 2013, University of St. Gallen, School of Economics and Political Science.
8. Yingheng Zhang & Haojie Li & Gang Ren, 2025. "Data-driven exploration of heterogeneous gasoline price elasticities using generalized random forests," Transportation, Springer, vol. 52(1), pages 215-237, February.
9. Semenova, Vira, 2025. "Generalized Lee bounds," Journal of Econometrics, Elsevier, vol. 251(C).
10. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Jan 2025.
11. Achim Ahrens & Victor Chernozhukov & Christian Hansen & Damian Kozbur & Mark Schaffer & Thomas Wiemann, 2025. "An Introduction to Double/Debiased Machine Learning," Papers 2504.08324, arXiv.org, revised Feb 2026.
    • Ahrens, Achim & Chernozhukov, Victor & Hansen, Christian & Kozbur, Damian & Schaffer, Mark & Wiemann, Thomas, 2026. "An Introduction to Double/Debiased Machine Learning," IZA Discussion Papers 18438, IZA Network @ LISER.
12. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Apr 2025.
13. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
14. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    • Bart Cockx & Michael Lechner & Joost Bollens, 2019. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Papers 1912.12864, arXiv.org, revised Dec 2022.
    • Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority of Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium," CESifo Working Paper Series 8297, CESifo.
    • Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 20/998, Ghent University, Faculty of Economics and Business Administration.
    • Lechner, Michael & Cockx, Bart & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," CEPR Discussion Papers 14270, C.E.P.R. Discussion Papers.
    • Cockx, Bart & Lechner, Michael & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," ROA Research Memorandum 006, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    • Cockx, Bart & Lechner, Michael & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Research Memorandum 015, Maastricht University, Graduate School of Business and Economics (GSBE).
    • Cockx, Bart & Lechner, Michael & Bollens, Joost, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Economics Working Paper Series 2001, University of St. Gallen, School of Economics and Political Science.
    • Cockx, Bart & Lechner, Michael & Bollens, Joost, 2019. "Priority to Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium," IZA Discussion Papers 12875, IZA Network @ LISER.
    • Bart Cockx & Michael Lechner & Joost Bollens, 2020. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," LIDAM Discussion Papers IRES 2020016, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
15. Mkondiwa, Maxwell & Kishore, Avinash & Veetil, Prakashan Chellattan & Sherpa, Sonam & Saxena, Satyam & Pinjarla, Bhavani & Urfels, Anton & Poonia, Shishpal & Ajay, Anurag & Craufurd, Peter & Malik, Ra, 2025. "Farmers agronomic management responses to extreme drought and rice yields in Bihar, India," Agricultural Water Management, Elsevier, vol. 320(C).
16. Henrika Langen & Martin Huber, 2023. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-37, January.
    • Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
17. Rupali Kaul & Stephen J. Anderson & Pradeep K. Chintagunta & Naufel Vilcassim, 2025. "Call Me Maybe: Does Customer Feedback Seeking Impact Nonsolicited Customers?," Marketing Science, INFORMS, vol. 44(1), pages 129-154, January.
    • Kaul, Rupali & Anderson, Stephen J. & Chintagunta, Pradeep K. & Vilcassim, Naufel, 2025. "Call me maybe: does customer feedback seeking impact nonsolicited customers?," LSE Research Online Documents on Economics 125615, London School of Economics and Political Science, LSE Library.
18. Keisuke Kawata & Mizuki Komura, 2023. "Only-child matching penalty in the marriage market," Papers 2307.15336, arXiv.org.
    • Kawata, Keisuke & Komura, Mizuki, 2025. "Only-Child Matching Penalty in the Marriage Market," IZA Discussion Papers 18198, IZA Network @ LISER.
    • Keisuke Kawata & Mizuki Komura, 2023. "Only-child matching penalty in the marriage market," Discussion Paper Series 254, School of Economics, Kwansei Gakuin University, revised Jun 2025.
19. Adam Baybutt, 2024. "Dynamic Latent-Factor Model with High-Dimensional Asset Characteristics," Papers 2405.15721, arXiv.org.
20. Patrick Rehill, 2024. "How do applied researchers use the Causal Forest? A methodological review of a method," Papers 2404.13356, arXiv.org, revised Dec 2024.
21. Lin Li & Kecheng Wei & Jiliang Han & Yuchun Zhu, 2025. "Digital empowerment and the development resilience in rural households: causal inference based on double machine learning," Empirical Economics, Springer, vol. 69(3), pages 1187-1227, September.
22. Taiyo Fukai & Keisuke Kawata & Mizuki Komura & Takahiro Toriyabe, 2024. "Gender gap in the ask salaries: Evidence from larger administrative data," Discussion Paper Series 284, School of Economics, Kwansei Gakuin University.
23. Patrick Rehill, 2024. "Distilling interpretable causal trees from causal forests," Papers 2408.01023, arXiv.org.
24. Franziska Zimmert & Michael Zimmert, 2024. "Part‐time subsidies and maternal reemployment: Evidence from a difference‐in‐differences analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(6), pages 1149-1171, September.
25. Paul B. Ellickson & Wreetabrata Kar & James C. Reeder, 2023. "Estimating Marketing Component Effects: Double Machine Learning from Targeted Digital Promotions," Marketing Science, INFORMS, vol. 42(4), pages 704-728, July.
26. Vira Semenova, 2023. "Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters," Papers 2303.00982, arXiv.org, revised May 2025.
27. Elena Kotyrlo, 2025. "Evaluation of continuous treatment," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 80, pages 93-116.
28. 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.
    • Heiler, Phillip & Knaus, Michael C., 2022. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," IZA Discussion Papers 15580, IZA Network @ LISER.
29. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
30. Abhinandan Dalal & Eric J. Tchetgen Tchetgen, 2025. "Partial Identification of Causal Effects for Endogenous Continuous Treatments," Papers 2508.13946, arXiv.org.
31. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
32. Adam Baybutt & Manu Navjeevan, 2023. "Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls," Papers 2301.06283, arXiv.org.
33. Hui Lan & Vasilis Syrgkanis, 2023. "Causal Q-Aggregation for CATE Model Selection," Papers 2310.16945, arXiv.org, revised Apr 2025.
34. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
35. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    • Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2022. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," NBER Working Papers 30302, National Bureau of Economic Research, Inc.
36. Chetverikov, Denis & Liu, Yukun & Tsyvinski, Aleh, 2025. "Weighted-average quantile regression," Journal of Econometrics, Elsevier, vol. 252(PA).
37. Yizhi Liu & Balaji Padmanabhan & Siva Viswanathan, 2026. "Estimating Visual Attribute Effects in Advertising from Observational Data: A Deepfake-Informed Double Machine Learning Approach," Papers 2603.02359, arXiv.org.
38. Amilcar Velez, 2024. "On the Asymptotic Properties of Debiased Machine Learning Estimators," Papers 2411.01864, arXiv.org.
39. Dmitry Arkhangelsky & Kazuharu Yanagimoto & Tom Zohar, 2024. "On Causal Inference with Model-Based Outcomes," Papers 2403.19563, arXiv.org, revised Jan 2026.
40. 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.
41. Tesary Lin & Avner Strulov-Shlain, 2023. "Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data," Papers 2308.13496, arXiv.org.
42. 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.
43. Liu, Nan & Liu, Yanbo & Sasaki, Yuya, 2026. "Estimation and inference for causal functions with multi-way clustered data," Journal of Econometrics, Elsevier, vol. 253(C).
    • Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
44. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
45. Kyle Myers & Wei Yang Tham, 2023. "Money, Time, and Grant Design," Papers 2312.06479, arXiv.org.
46. Philipp Schwarz & Oliver Schacht & Sven Klaassen & Daniel Grunbaum & Sebastian Imhof & Martin Spindler, 2024. "Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?," Papers 2406.11308, arXiv.org.
47. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
48. Yao Cui & Andrew M. Davis, 2022. "Tax-Induced Inequalities in the Sharing Economy," Management Science, INFORMS, vol. 68(10), pages 7202-7220, October.
49. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.
50. Patrick Rehill & Nicholas Biddle, 2024. "Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning," Papers 2401.07075, arXiv.org, revised Mar 2024.
51. Dana Turjeman & Fred M. Feinberg, 2024. "When the Data Are Out: Measuring Behavioral Changes Following a Data Breach," Marketing Science, INFORMS, vol. 43(2), pages 440-461, March.
52. Vishalie Shah & Julia Hatamyar & Taufik Hidayat & Noemi Kreif, 2025. "Exploring the heterogeneous impacts of Indonesia's conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests," Papers 2501.12803, arXiv.org.
53. Ang Yu & Felix Elwert, 2023. "Nonparametric Causal Decomposition of Group Disparities," Papers 2306.16591, arXiv.org, revised Dec 2024.
54. Di Liu, 2024. "Treatment-effects estimation using lasso," Chinese Stata Conference 2024 09, Stata Users Group.
55. 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.
56. Max Vilgalys, 2023. "A Machine Learning Approach to Measuring Climate Adaptation," Papers 2302.01236, arXiv.org.
57. Maliar, Serguei & Salanié, Bernard, 2024. "Testing for Asymmetric Information in Insurance with Deep Learning," CEPR Discussion Papers 19105, C.E.P.R. Discussion Papers.
    • Serguei Maliar & Bernard Salanie, 2024. "Testing for Asymmetric Information in Insurance with Deep Learning," Papers 2404.18207, arXiv.org.
58. 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.
59. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org, revised Oct 2025.
60. Lucas Z. Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org, revised Dec 2025.
61. Heiler, Phillip, 2024. "Heterogeneous treatment effect bounds under sample selection with an application to the effects of social media on political polarization," Journal of Econometrics, Elsevier, vol. 244(1).
    • 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.
62. 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.
63. Martin Huber & Jannis Kueck, 2022. "Testing the identification of causal effects in observational data," Papers 2203.15890, arXiv.org, revised Jun 2023.