[1] |
Altman N, Krzywinski M. Association, correlation and causation. Nature Methods, 2015, 12 (10): 899–900. doi: 10.1038/nmeth.3587
|
[2] |
Zhang L, Zou H, Zhao Y, et al. Association between blood circulating vitamin D and colorectal cancer risk in Asian countries: A systematic review and dose-response meta-analysis. BMJ Open, 2019, 9 (12): e030513. doi: 10.1136/bmjopen-2019-030513
|
[3] |
Athey S, Tibshirani J, Wager S. Generalized random forests. The Annals of Statistics, 2019, 47 (2): 1148–1178. doi: 10.1214/18-AOS1709
|
[4] |
Künzel S R, Sekhon J S, Bickel P J, et al. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 2019, 116 (10): 4156–4165. doi: 10.1073/pnas.1804597116
|
[5] |
Athey S, Imbens G. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 2016, 113 (27): 7353–7360. doi: 10.1073/pnas.1510489113
|
[6] |
Robinson P M. Root-N-consistent semiparametric regression. Econometrica, 1988: 931–954. doi: 10.2307/1912705
|
[7] |
Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 2018, 113 (523): 1228–1242. doi: 10.1080/01621459.2017.1319839
|
[8] |
Fan Y, Lv J, Wang J. DNN: A two-scale distributional tale of heterogeneous treatment effect inference. SSRN 3238897, 2018.
|
[9] |
Johansson F, Shalit U, Sontag D. Learning representations for counterfactual inference. In: Proceedings of the 33rd International Conference on Machine Learning. New York: PMLR, 2016: 3020–3029.
|
[10] |
Shalit U, Johansson F D, Sontag D. Estimating individual treatment effect: Generalization bounds and algorithms. In: Proceedings of the 34th International Conference on Machine Learning. Sydney: PMLR, 2017: 3076–3085.
|
[11] |
Zhang Z, Lan Q, Ding L, et al. Reducing selection bias in counterfactual reasoning for individual treatment effects estimation. arXiv: 1912.09040, 2019.
|
[12] |
Atan O, Jordon J, van der Schaar M. Deep-treat: Learning optimal personalized treatments from observational data using neural networks. In: Thirty-Second AAAI Conference on Artificial Intelligence. Palo Alto, CA: Association for the Advancement of Artificial Intelligence, 2018: 2071–2078.
|
[13] |
Su X, Tsai C L, Wang H, et al. Subgroup analysis via recursive partitioning. Journal of Machine Learning Research, 2009, 10: 141–158. doi: 10.5555/1577069.1577074
|
[14] |
Yang J, Dahabreh I J, Steingrimsson J A. Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data. Biometrics, 2022, 78 (2): 624–635. doi: 10.1111/biom.13432
|
[15] |
Foster J C, Taylor J M, Ruberg S J. Subgroup identification from randomized clinical trial data. Statistics in Medicine, 2011, 30 (24): 2867–2880. doi: 10.1002/sim.4322
|
[16] |
Breiman L, Friedman J, Olshen R, et al. Classification and regression trees. Belmont, CA: Wadsworth International Group, 1984, 37(15): 237–251.
|
[17] |
Chernozhukov V, Demirer M, Duflo E, et al. Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immunization in India. Cambridge, MA: National Bureau of Economic Research, 2018.
|
[18] |
Park C, Kang H. A groupwise approach for inferring heterogeneous treatment effects in causal inference. arXiv: 1908.04427, 2019.
|
[19] |
Rubin D B. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 1974, 66 (5): 688–701. doi: 10.1037/h0037350
|
[20] |
Imbens G W, Rubin D B. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge University Press, 2015.
|
[21] |
Hernán M A, Robins J M. Causal Inference: What If. Boca Raton, FL: Chapman & Hall/CRC, 2020.
|
[22] |
Chernozhukov V, Chetverikov D, Demirer M, et al. Double/ debiased/neyman machine learning of treatment effects. American Economic Review, 2017, 107 (5): 261–65. doi: 10.1257/aer.p20171038
|
[23] |
Nie X, Wager S. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika, 2021, 108 (2): 299–319. doi: 10.1093/biomet/asaa076
|
[24] |
Berk R, Brown L, Buja A, et al. Valid post-selection inference. The Annals of Statistics, 2013, 41 (2): 802–837. doi: 10.1214/12-AOS1077
|
[25] |
Lee J D, Sun D L, Sun Y, et al. Exact post-selection inference, with application to the lasso. The Annals of Statistics, 2016, 44 (3): 907–927. doi: 10.1214/15-AOS1371
|
[26] |
Fithian W, Sun D, Taylor J. Optimal inference after model selection. arXiv: 1410.2597, 2014.
|
[27] |
Hothorn T, Bretz F, Westfall P. Simultaneous inference in general parametric models. Biometrical Journal, 2008, 50 (3): 346–363. doi: 10.1002/bimj.200810425
|
[28] |
Guerrero E G. Enhancing access and retention in substance abuse treatment: the role of medicaid payment acceptance and cultural competence. Drug and Alcohol Dependence, 2013, 132 (3): 555–561. doi: 10.1016/j.drugalcdep.2013.04.005
|
[29] |
Kong Y, Zhou J, Zheng Z, et al. Using machine learning to advance disparities research: Subgroup analyses of access to opioid treatment. Health Services Research, 2022, 57 (2): 411–421. doi: 10.1111/1475-6773.13896
|
[1] |
Altman N, Krzywinski M. Association, correlation and causation. Nature Methods, 2015, 12 (10): 899–900. doi: 10.1038/nmeth.3587
|
[2] |
Zhang L, Zou H, Zhao Y, et al. Association between blood circulating vitamin D and colorectal cancer risk in Asian countries: A systematic review and dose-response meta-analysis. BMJ Open, 2019, 9 (12): e030513. doi: 10.1136/bmjopen-2019-030513
|
[3] |
Athey S, Tibshirani J, Wager S. Generalized random forests. The Annals of Statistics, 2019, 47 (2): 1148–1178. doi: 10.1214/18-AOS1709
|
[4] |
Künzel S R, Sekhon J S, Bickel P J, et al. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 2019, 116 (10): 4156–4165. doi: 10.1073/pnas.1804597116
|
[5] |
Athey S, Imbens G. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 2016, 113 (27): 7353–7360. doi: 10.1073/pnas.1510489113
|
[6] |
Robinson P M. Root-N-consistent semiparametric regression. Econometrica, 1988: 931–954. doi: 10.2307/1912705
|
[7] |
Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 2018, 113 (523): 1228–1242. doi: 10.1080/01621459.2017.1319839
|
[8] |
Fan Y, Lv J, Wang J. DNN: A two-scale distributional tale of heterogeneous treatment effect inference. SSRN 3238897, 2018.
|
[9] |
Johansson F, Shalit U, Sontag D. Learning representations for counterfactual inference. In: Proceedings of the 33rd International Conference on Machine Learning. New York: PMLR, 2016: 3020–3029.
|
[10] |
Shalit U, Johansson F D, Sontag D. Estimating individual treatment effect: Generalization bounds and algorithms. In: Proceedings of the 34th International Conference on Machine Learning. Sydney: PMLR, 2017: 3076–3085.
|
[11] |
Zhang Z, Lan Q, Ding L, et al. Reducing selection bias in counterfactual reasoning for individual treatment effects estimation. arXiv: 1912.09040, 2019.
|
[12] |
Atan O, Jordon J, van der Schaar M. Deep-treat: Learning optimal personalized treatments from observational data using neural networks. In: Thirty-Second AAAI Conference on Artificial Intelligence. Palo Alto, CA: Association for the Advancement of Artificial Intelligence, 2018: 2071–2078.
|
[13] |
Su X, Tsai C L, Wang H, et al. Subgroup analysis via recursive partitioning. Journal of Machine Learning Research, 2009, 10: 141–158. doi: 10.5555/1577069.1577074
|
[14] |
Yang J, Dahabreh I J, Steingrimsson J A. Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data. Biometrics, 2022, 78 (2): 624–635. doi: 10.1111/biom.13432
|
[15] |
Foster J C, Taylor J M, Ruberg S J. Subgroup identification from randomized clinical trial data. Statistics in Medicine, 2011, 30 (24): 2867–2880. doi: 10.1002/sim.4322
|
[16] |
Breiman L, Friedman J, Olshen R, et al. Classification and regression trees. Belmont, CA: Wadsworth International Group, 1984, 37(15): 237–251.
|
[17] |
Chernozhukov V, Demirer M, Duflo E, et al. Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immunization in India. Cambridge, MA: National Bureau of Economic Research, 2018.
|
[18] |
Park C, Kang H. A groupwise approach for inferring heterogeneous treatment effects in causal inference. arXiv: 1908.04427, 2019.
|
[19] |
Rubin D B. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 1974, 66 (5): 688–701. doi: 10.1037/h0037350
|
[20] |
Imbens G W, Rubin D B. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge, UK: Cambridge University Press, 2015.
|
[21] |
Hernán M A, Robins J M. Causal Inference: What If. Boca Raton, FL: Chapman & Hall/CRC, 2020.
|
[22] |
Chernozhukov V, Chetverikov D, Demirer M, et al. Double/ debiased/neyman machine learning of treatment effects. American Economic Review, 2017, 107 (5): 261–65. doi: 10.1257/aer.p20171038
|
[23] |
Nie X, Wager S. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika, 2021, 108 (2): 299–319. doi: 10.1093/biomet/asaa076
|
[24] |
Berk R, Brown L, Buja A, et al. Valid post-selection inference. The Annals of Statistics, 2013, 41 (2): 802–837. doi: 10.1214/12-AOS1077
|
[25] |
Lee J D, Sun D L, Sun Y, et al. Exact post-selection inference, with application to the lasso. The Annals of Statistics, 2016, 44 (3): 907–927. doi: 10.1214/15-AOS1371
|
[26] |
Fithian W, Sun D, Taylor J. Optimal inference after model selection. arXiv: 1410.2597, 2014.
|
[27] |
Hothorn T, Bretz F, Westfall P. Simultaneous inference in general parametric models. Biometrical Journal, 2008, 50 (3): 346–363. doi: 10.1002/bimj.200810425
|
[28] |
Guerrero E G. Enhancing access and retention in substance abuse treatment: the role of medicaid payment acceptance and cultural competence. Drug and Alcohol Dependence, 2013, 132 (3): 555–561. doi: 10.1016/j.drugalcdep.2013.04.005
|
[29] |
Kong Y, Zhou J, Zheng Z, et al. Using machine learning to advance disparities research: Subgroup analyses of access to opioid treatment. Health Services Research, 2022, 57 (2): 411–421. doi: 10.1111/1475-6773.13896
|