{"id":51658,"date":"2024-07-19T21:32:11","date_gmt":"2024-07-19T13:32:11","guid":{"rendered":"https:\/\/swarma.org\/?p=51658"},"modified":"2024-07-19T21:32:11","modified_gmt":"2024-07-19T13:32:11","slug":"%e5%a4%a7%e8%af%ad%e8%a8%80%e6%a8%a1%e5%9e%8b%e7%94%a8%e4%ba%8e%e5%9b%a0%e6%9e%9c%e5%8f%91%e7%8e%b0%e5%92%8c%e5%9b%a0%e6%9e%9c%e6%8e%a8%e6%96%ad-%e5%91%a8%e6%97%a5%e7%9b%b4%e6%92%ad%c2%b7%e5%9b%a0","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=51658","title":{"rendered":"\u5927\u8bed\u8a00\u6a21\u578b\u7528\u4e8e\u56e0\u679c\u53d1\u73b0\u548c\u56e0\u679c\u63a8\u65ad | \u5468\u65e5\u76f4\u64ad\u00b7\u56e0\u679c\u79d1\u5b66\u4e0e\u5927\u8bed\u8a00\u6a21\u578b\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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[1] Long, Stephanie, et al. “Causal Discovery with Language Models as Imperfect Experts.<\/strong>” ICML Workshop, 2023.<\/span><\/p>\n

[2] Abdulaal, Ahmed, et al. “Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata-and Data-driven Reasoning.<\/strong>” ICLR, 2023.<\/span><\/p>\n

[3] Cai, Hengrui, et al. “Is Knowledge All Large Language Models Needed for Causal Reasoning?.<\/strong>” arXiv preprint arXiv:2401.00139 (2023).<\/span><\/p>\n

[4] Vashishtha, Aniket, et al. “Causal Inference using LLM-Guided Discovery.<\/strong>” AAAI Workshop, 2023.<\/span><\/p>\n

[5] Liu, Chenxi, et al. “Discovery of the Hidden World with Large Language Models.<\/strong>” arXiv preprint arXiv:2402.03941 (2024).<\/span><\/p>\n

[6] Jin, Zhijing, et al. “CLADDER: Assessing causal reasoning in language models.<\/strong>” NeurIPS, 2023.<\/span><\/p>\n

[7] Liu, Xiao, et al. “Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data.<\/strong>” arXiv preprint arXiv:2402.17644 (2024).<\/em><\/span><\/p>\n

[8] Jiang, Haitao, et al. “Large Language Model for Causal Decision Making.<\/strong>” arXiv preprint arXiv:2312.17122 (2023).<\/span><\/section>\n

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\u201c\u56e0\u679c\u201d\u5e76\u4e0d\u662f\u4e00\u4e2a\u65b0\u6982\u5ff5\uff0c\u800c\u662f\u4e00\u4e2a\u5df2\u7ecf\u5728\u591a\u4e2a\u5b66\u79d1\u4e2d\u4f7f\u7528\u4e86\u6570\u5341\u5e74\u7684\u5206\u6790\u6280\u672f\u3002\u96c6\u667a\u4ff1\u4e50\u90e8\u5728\u8fc7\u53bb4\u5e74\u671f\u95f4\u56f4\u7ed5\u7814\u7a76\u4eba\u5458\u7684\u4e0d\u540c\u89d2\u5ea6\u7684\u9700\u6c42\uff0c\u4e3e\u529e\u4e864\u5b63\u76f8\u5173\u4e3b\u9898\u7684\u8bfb\u4e66\u4f1a\uff0c\u5f62\u6210\u4e86\u6570\u5343\u4eba\u89c4\u6a21\u7684\u793e\u533a\u3002<\/span><\/section>\n

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\u3010<\/span>\u7b2c\u4e00\u5b63\uff1a\u56e0\u679c\u79d1\u5b66\u4e0eCausal AI<\/span><\/a>\u3011\u57fa\u4e8e\u300aElements of Causal Inference\u300b\uff0c\u63a2\u8ba8\u56e0\u679c\u79d1\u5b66\u5728\u673a\u5668\u5b66\u4e60\u65b9\u9762\u7684\u5e94\u7528\uff0c\u5982\u5f3a\u5316\u5b66\u4e60\u548c\u8fc1\u79fb\u5b66\u4e60\u7b49\uff0c\u5e76\u5206\u4eab\u5de5\u4e1a\u5e94\u7528\u3002<\/span><\/span><\/p>\n

\u3010<\/span>\u7b2c\u4e8c\u5b63\uff1a\u56e0\u679c\u79d1\u5b66\u4e0e\u57fa\u7840\u5b9e\u6218<\/span><\/a>\u3011\u805a\u7126\u5b9e\u64cd\u548c\u57fa\u7840\uff0c\u6df1\u5165\u5b66\u4e60\u300aCausal inference in statistics: A primer\u300b\u548c\u300aElements of causal inference: foundations and learning algorithms\u300b\u3002 <\/span><\/span><\/p>\n

\u3010<\/span>\u7b2c\u4e09\u5b63\uff1a\u56e0\u679c\u79d1\u5b66\u4e0eCausal +X<\/span><\/a>\u3011\u56de\u987e\u793e\u4f1a\u5b66\u3001\u7ecf\u6d4e\u5b66\u3001\u533b\u5b66\uff0c\u8ba1\u7b97\u673a\u7b49\u9886\u57df\u7684\u56e0\u679c\u6a21\u578b\u548c\u8303\u5f0f\uff0c\u5c1d\u8bd5\u7528\u73b0\u4ee3\u6a21\u578b\u63d0\u4f9b\u65b0\u601d\u8def\u3002 <\/span><\/span><\/p>\n

\u3010<\/span>\u7b2c\u56db\u5b63\uff1a\u56e0\u679c\u8868\u5f81\u5b66\u4e60<\/span><\/a>\u3011\u63a2\u8ba8\u56e0\u679c\u8868\u5f81\u5b66\u4e60\u7684\u7406\u8bba\u3001\u6280\u672f\u548c\u6700\u65b0\u5e94\u7528\uff0c\u6d89\u53ca\u56e0\u679c\u751f\u6210\u6a21\u578b\u3001\u53ef\u89e3\u91ca\u6027\u3001\u516c\u5e73\u6027\u53ca\u5de5\u4e1a\u843d\u5730\u3002<\/span><\/span><\/p>\n

\u7b2c\u4e94\u5b63\u8bfb\u4e66\u4f1a<\/strong><\/span><\/a>\u4e3b\u8981\u56f4\u7ed5\u56e0\u679c\u79d1\u5b66\u7684\u6700\u65b0\u8fdb\u5c55\uff0c\u5305\u62ec\u56e0\u679c\u79d1\u5b66\u4e0e\u5927\u6a21\u578b\u7684\u7ed3\u5408\u7b49\u65b9\u9762\u8fdb\u884c\u6df1\u5ea6\u7684\u63a2\u8ba8\u548c\u68b3\u7406\uff0c\u5e0c\u671b\u7ed9\u5728\u8fd9\u4e2a\u9886\u57df\u7684\u7814\u7a76\u8005\u63d0\u4f9b\u4e00\u4e2a\u5168\u9762\u7684\u7814\u7a76\u56fe\u666f\u3002\u5171\u540c\u63a2\u8ba8\u56e0\u679c\u79d1\u5b66\u7684\u672a\u6765\u53d1\u5c55\u4ee5\u53ca\u9762\u4e34\u7684\u6311\u6218\u3002<\/span><\/section>\n

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\u8be6\u60c5\u8bf7\u89c1\uff1a<\/span>\u901f\u6765\uff01\u56e0\u679c\u4e0e\u5927\u6a21\u578b\u7684\u53cc\u5411\u8d4b\u80fd\u4e28\u56e0\u679c\u79d1\u5b66\u7b2c\u4e94\u5b63\u5f3a\u52bf\u56de\u5f52<\/a><\/span><\/p>\n


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