{"id":40346,"date":"2023-01-31T22:24:06","date_gmt":"2023-01-31T14:24:06","guid":{"rendered":"https:\/\/swarma.org\/?p=40346"},"modified":"2023-01-31T22:24:06","modified_gmt":"2023-01-31T14:24:06","slug":"causalai%e6%a0%b8%e5%bf%83%e7%ae%97%e6%b3%95%ef%bc%9a%e5%9b%a0%e6%9e%9c%e6%a3%ae%e6%9e%97%e5%8f%8a%e5%85%b6%e7%9b%b8%e5%85%b3%e7%a0%94%e7%a9%b6","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=40346","title":{"rendered":"CausalAI\u6838\u5fc3\u7b97\u6cd5\uff1a\u56e0\u679c\u68ee\u6797\u53ca\u5176\u76f8\u5173\u7814\u7a76"},"content":{"rendered":"
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\u5728\u96c6\u667a\u4ff1\u4e50\u90e8\u56e0\u679c\u79d1\u5b66\u8bfb\u4e66\u4f1a\u7b2c\u4e09\u5b63<\/a>\uff0c<\/strong><\/strong><\/strong>\u4e2d\u56fd\u79d1\u5b66\u6280\u672f\u5927\u5b66\u7edf\u8ba1\u5b66\u535a\u58eb\u9f9a\u9e64\u626c\u4ecb\u7ecd\u4e86\u56e0\u679c\u6811\u3001\u56e0\u679c\u68ee\u6797\u53ca\u5feb\u624b\u7684\u5927\u89c4\u6a21\u591a\u5143\u56e0\u679c\u68ee\u6797\u6a21\u578b\u3002\u56e0\u679c\u6811\u6a21\u578b\u7684\u4e3b\u8981\u8d21\u732e\u662f\u201c\u8bda\u5b9e\u201d (Honest) \u7684\u65b9\u6cd5\uff0c\u4f7f\u7528\u975e\u8bad\u7ec3\u6837\u672c\u4f30\u8ba1\u6a21\u578b\u7684\u5c40\u90e8\u53c2\u6570\u3002\u76f8\u8f83\u4e8e\u4f20\u7edf\u7684\u201c\u81ea\u4fe1\u201d (Adaptive) \u7684\u65b9\u6cd5\uff0c\u201c\u8bda\u5b9e\u201d\u7684\u65b9\u6cd5\u6709\u8f83\u597d\u7684\u6cdb\u5316\u80fd\u529b\u3002\u5e7f\u4e49\u968f\u673a\u68ee\u6797\u4ee5\u56e0\u679c\u6811\u6a21\u578b\u4e3a\u57fa\u7840\uff0c\u7ed3\u5408\u6240\u5173\u6ce8\u7684\u7279\u5b9a\u95ee\u9898\uff0c\u6784\u5efa\u7b97\u6cd5\u6846\u67b6\u3002\u5feb\u624b\u7684\u5927\u89c4\u6a21\u591a\u5143\u56e0\u679c\u68ee\u6797\u53ef\u4ee5\u5bf9\u5355\u4e00\u4e00\u4e2a\u6a21\u578b\u540c\u65f6\u5904\u7406\u4efb\u610f\u79cd\u5e72\u9884\u624b\u6bb5\uff0c<\/strong>\u540c\u65f6HTE\u7684\u5b9a\u4e49\u8981\u6c42\u5404\u5e72\u9884\u624b\u6bb5\u5bf9\u5e94\u4e00\u81f4\u7684\u7279\u5f81\u5b50\u7a7a\u95f4\u3002\u672c\u6587\u662f\u6839\u636e\u6b64\u6b21\u5206\u4eab\u6574\u7406\u7684\u6587\u5b57\u7a3f\u3002<\/strong><\/strong><\/strong><\/strong><\/p>\n


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\u7814\u7a76\u9886\u57df\uff1a\u56e0\u679c\u6811\u6a21\u578b\uff0c\u56e0\u679c\u68ee\u6797<\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/span><\/strong><\/span><\/strong><\/span><\/strong><\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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\u9f9a\u9e64\u626c<\/span> <\/span>| \u8bb2\u8005<\/strong><\/span><\/strong><\/p>\n

\u8d3e\u6f4d\u4f73 <\/span>| <\/strong><\/span>\u6574\u7406<\/strong><\/span><\/p>\n

\u9093\u4e00\u96ea <\/span>| <\/strong><\/span>\u7f16\u8f91<\/strong><\/span><\/p>\n<\/section>\n


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1. \u56e0\u679c\u6811<\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u56fe1. \u56e0\u679c\u6811\u793a\u4f8b<\/span><\/section>\n

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\u90a3\u4e48\uff0c\u4e3a\u4ec0\u4e48\u8981\u7528\u201c\u8bda\u5b9e\u201d\u7684\u65b9\u6cd5\u800c\u4e0d\u76f4\u63a5\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\uff1f\u4e00\u65b9\u9762\uff0c\u56e0\u4e3a\u4f7f\u7528\u7684\u6837\u672c\u662f\u72ec\u7acb\u7684\uff0c\u56e0\u6b64\u5728\u4f30\u8ba1\u5b50\u7a7a\u95f4\u4e0a\u7684\u56e0\u679c\u6548\u5e94\u65f6\uff0c\u4f7f\u7528\u201c\u8bda\u5b9e\u201d\u7684\u65b9\u6cd5\u5f97\u5230\u7684\u7ed3\u679c\u4f1a\u662f\u65e0\u504f\u7684\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u5b9e\u9a8c\u7ed3\u679c\u663e\u793a\uff0c\u201c\u8bda\u5b9e\u201d\u7684\u65b9\u6cd5\u76f8\u8f83\u4e8e\u975e\u201c\u8bda\u5b9e\u201d\u7684\u65b9\u6cd5\u80fd\u591f\u8986\u76d6\u66f4\u5927\u7684\u7f6e\u4fe1\u533a\u95f4\u3002\u4e5f\u5c31\u662f\u8bf4\uff0c\u56e0\u679c\u6811\u5177\u5907\u826f\u597d\u7684\u7edf\u8ba1\u6027\u8d28\u3002<\/span><\/section>\n

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2. \u5e7f\u4e49\u968f\u673a\u68ee\u6797<\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u56fe4. \u5e7f\u4e49\u968f\u673a\u68ee\u6797\u7b97\u6cd5\u6846\u67b6<\/span><\/section>\n

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\u56fe5. \u5de5\u5177\u53d8\u91cf\u6cd5\u7684\u56e0\u679c\u56fe<\/span><\/section>\n

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\u56fe6. \u5de5\u5177\u53d8\u91cf\u7684\u5f97\u5206\u51fd\u6570<\/span><\/section>\n

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\u7efc\u4e0a\uff0c\u56e0\u679c\u6811\u533a\u522b\u4e8e\u666e\u901a\u7684\u673a\u5668\u5b66\u4e60\u6811\u6a21\u578b\u7684\u70b9\u5728\u4e8e\uff0c\u4f30\u8ba1\u7684\u5185\u5bb9\u662f\u5b50\u7a7a\u95f4\u7684\u56e0\u679c\u6548\u5e94\uff0c\u4e0e\u589e\u76ca\u6811\u7684\u6700\u5927\u533a\u522b\u5728\u4e8e\u201c\u8bda\u5b9e\u201d\u7684\u65b9\u6cd5\u907f\u514d\u4e86\u8fc7\u62df\u5408\u3002<\/span><\/section>\n

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3. \u4ece\u56e0\u679c\u6811\u5230\u5e7f\u4e49\u968f\u673a\u68ee\u6797<\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u56e0\u679c\u6811\u7684\u4e3b\u8981\u8d21\u732e\u662f\u91c7\u7528\u201c\u8bda\u5b9e\u201d\u7684\u65b9\u6cd5\uff0c\u4f7f\u7528\u975e\u8bad\u7ec3\u6837\u672c\u4f30\u8ba1\u6a21\u578b\u7684\u5c40\u90e8\u53c2\u6570\uff1b\u56e0\u679c\u68ee\u6797\u7684\u4e3b\u8981\u8d21\u732e\u662f\u9996\u6b21\u5c06\u7edf\u8ba1\u5b66\u53ef\u8bc1\u660e\u7684\u65b9\u6cd5\u5f15\u5165\u6a21\u578b\uff0c\u5176\u4e2d\uff0c\u5e7f\u4e49\u968f\u673a\u68ee\u6797\u662f\u7ed3\u5408\u6240\u5173\u6ce8\u7279\u5b9a\u95ee\u9898\u7ed3\u6784\u7684\u673a\u5668\u5b66\u4e60\u6811\u7c7b\u6a21\u578b\u7684\u7b97\u6cd5\u6846\u67b6\u3002<\/span><\/section>\n

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4. \u5feb\u624b\u7684\u5927\u89c4\u6a21\u591a\u5143\u56e0\u679c\u68ee\u6797[2]<\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u667a\u80fd\u8425\u9500\u8981\u7814\u7a76\u7684\u6838\u5fc3\u95ee\u9898\u662f\uff0c\u7528\u6237\u5bf9\u4e0d\u540c\u8865\u8d34\u989d\u5ea6\u7684\u8f6c\u5316\u6548\u679c\u5dee\u5f02\u6709\u591a\u5927\uff1f\u8fd9\u4e9b\u4e0d\u540c\u7684\u8865\u8d34\u989d\u5ea6\u53ef\u4ee5\u88ab\u770b\u4f5c\u662f\u56e0\u679c\u63a8\u65ad\u4e2d\u7684 treatments\uff0c\u6240\u4ee5\u573a\u666f\u9a71\u4f7f\u7814\u7a76\u8005\u53bb\u7814\u7a76\u7528\u6237\u5728\u4e0d\u540c treatments \u4e0b\u7684\u8f6c\u5316\u6548\u679c\uff0c\u5373\u9700\u8981\u591a\u5143\u56e0\u679c\u6a21\u578b\u3002<\/span><\/section>\n

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5. \u8bba\u6587\u63a8\u8350<\/span><\/strong><\/strong><\/p>\n<\/section>\n

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Causal Forest \u76f8\u5173\u6587\u732e<\/span><\/p>\n

    \n
  1. \n

    Athey S, Imbens G. Recursive partitioning for heterogeneous causal effects[J]. Proceedings of the National Academy of Sciences, 2016, 113(27): 7353-7360.<\/span><\/p>\n<\/li>\n

  2. \n

    Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests[J]. Journal of the American Statistical Association, 2018, 113(523): 1228-1242.<\/span><\/p>\n<\/li>\n

  3. \n

    Athey S, Tibshirani J, Wager S. Generalized random forests[J]. The Annals of Statistics, 2019, 47(2): 1148-1178.<\/span><\/p>\n<\/li>\n

  4. \n

    Athey S, Imbens G W. Machine learning methods that economists should know about[J]. Annual Review of Economics, 2019, 11: 685-725.<\/span><\/p>\n<\/li>\n

  5. \n

    Tran C, Zheleva E. Learning triggers for heterogeneous treatment effects[C]\/\/Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33(01): 5183-5190.<\/span><\/p>\n<\/li>\n

  6. \n

    Evidence-Based Policy Learning\uff08CLeaR 2022 Oral\uff09<\/span><\/p>\n<\/li>\n

  7. \n

    Nandy P, Yu X, Liu W, et al. Generalized Causal Tree for Uplift Modeling[J]. arXiv preprint arXiv:2202.02416, 2022.<\/span><\/p>\n<\/li>\n

  8. \n

    Tan X, Chang C C H, Tang L. A tree-based federated learning approach for personalized treatment effect estimation from heterogeneous data sources[J]. arXiv preprint arXiv:2103.06261, 2021.<\/span><\/p>\n<\/li>\n

  9. \n

    Zeng S, Bayir M A, Pfeiffer III J J, et al. Causal transfer random forest: Combining logged data and randomized experiments for robust prediction[C]\/\/Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 2021: 211-219.<\/span><\/p>\n<\/li>\n<\/ol>\n

    \u56e0\u679c\u4e4b\u68af: \u7ed3\u6784\u56e0\u679c\u6a21\u578b\u6587\u732e\u63a8\u8350<\/span><\/p>\n

      \n
    1. \n

      Pearl J. The seven tools of causal inference, with reflections on machine learning[J]. Communications of the ACM, 2019, 62(3): 54-60.<\/span><\/p>\n<\/li>\n

    2. \n

      Sch\u00f6lkopf B. Causality for machine learning[M]\/\/Probabilistic and Causal Inference: The Works of Judea Pearl. 2022: 765-804.<\/span><\/p>\n<\/li>\n

    3. \n

      On Pearl’s Hierarchy and the Foundations of Causal Inference<\/span><\/p>\n<\/li>\n

    4. \n

      Bongers, Stephan, et al. “Foundations of structural causal models with cycles and latent variables.” The Annals of Statistics 49.5 (2021): 2885-2915.<\/span><\/p>\n<\/li>\n<\/ol>\n


      <\/section>\n
      \u53c2\u8003\u6587\u732e<\/span><\/strong><\/span><\/section>\n

      1. Athey S, Imbens G. Recursive partitioning for heterogeneous causal effects[J]. Proceedings of the National Academy of Sciences, 2016, 113(27): 7353-7360.<\/span><\/p>\n

      2. Ai M, Li B, Gong H, et al. LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm[C]\/\/Proceedings of the ACM Web Conference 2022. 2022: 2310-2319.<\/span><\/p>\n


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