{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T03:15:07Z","timestamp":1777173307400,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,6]],"date-time":"2022-11-06T00:00:00Z","timestamp":1667692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11,6]]},"DOI":"10.1145\/3560905.3568304","type":"proceedings-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T23:37:10Z","timestamp":1674603430000},"page":"966-972","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["CIPhy"],"prefix":"10.1145","author":[{"given":"Zhizhang","family":"Hu","sequence":"first","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Yu","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Duke University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shijia","family":"Pan","sequence":"additional","affiliation":[{"name":"University of California"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517351.2517370"},{"key":"e_1_3_2_1_2_1","volume-title":"Causalml: Python package for causal machine learning. arXiv preprint arXiv:2002.11631","author":"Chen Huigang","year":"2020","unstructured":"Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, and Zhenyu Zhao. 2020. Causalml: Python package for causal machine learning. arXiv preprint arXiv:2002.11631 (2020)."},{"key":"e_1_3_2_1_3_1","volume-title":"The analysis of relationships involving dichotomous dependent variables. Journal of Health and Social Behavior","author":"Cleary Paul D","year":"1984","unstructured":"Paul D Cleary and Ronald Angel. 1984. The analysis of relationships involving dichotomous dependent variables. Journal of Health and Social Behavior (1984), 334--348."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","unstructured":"Yiwen Dong Shijia Pan Tong Yu Mostafa Mirshekari Jonathon Fagert Amelie Bonde Ole J. Mengshoel Pei Zhang and Hae Young Noh. 2021. The FootprintID Dataset: Footstep-Induced Structural Vibration Data for Indoor Person Identification with Different Walking Speeds. 10.5281\/zenodo.4691144","DOI":"10.5281\/zenodo.4691144"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3360322.3360858"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/AUTOID.2007.380623"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306618.3317950"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3494117"},{"key":"e_1_3_2_1_9_1","volume-title":"Causal direction of data collection matters: Implications of causal and anticausal learning for NLP. arXiv preprint arXiv:2110.03618","author":"Jin Zhijing","year":"2021","unstructured":"Zhijing Jin, Julius von K\u00fcgelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, and Bernhard Sch\u00f6lkopf. 2021. Causal direction of data collection matters: Implications of causal and anticausal learning for NLP. arXiv preprint arXiv:2110.03618 (2021)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3015812.3015840"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.11248"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3242587.3242609"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(82)90019-7"},{"key":"e_1_3_2_1_14_1","volume-title":"Applied logistic regression analysis. Number 106","author":"Menard Scott","unstructured":"Scott Menard. 2002. Applied logistic regression analysis. Number 106. Sage."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550284"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3130954"},{"key":"e_1_3_2_1_17_1","unstructured":"Judea Pearl. 1998. Why there is no statistical test for confounding why many think there is and why they are almost right. (1998)."},{"key":"e_1_3_2_1_18_1","unstructured":"Judea Pearl. 2009. Causality. Cambridge university press."},{"key":"e_1_3_2_1_19_1","volume-title":"Kuk Lida Lee, and Gary M Ingersoll","author":"Joanne Peng Chao-Ying","year":"2002","unstructured":"Chao-Ying Joanne Peng, Kuk Lida Lee, and Gary M Ingersoll. 2002. An introduction to logistic regression analysis and reporting. The journal of educational research 96, 1 (2002), 3--14."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Bernhard Sch\u00f6lkopf. 2022. Causality for machine learning. In Probabilistic and Causal Inference: The Works of Judea Pearl. 765--804.","DOI":"10.1145\/3501714.3501755"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1951.tb00088.x"},{"key":"e_1_3_2_1_22_1","volume-title":"Domain Adaptation in Computer Vision Applications","author":"Sun Baochen","unstructured":"Baochen Sun, Jiashi Feng, and Kate Saenko. 2017. Correlation alignment for unsupervised domain adaptation. In Domain Adaptation in Computer Vision Applications. Springer, 153--171."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1177\/1420326X19875621"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01077"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2016.7460727"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i3.20271"}],"event":{"name":"SenSys '22: The 20th ACM Conference on Embedded Networked Sensor Systems","location":"Boston Massachusetts","acronym":"SenSys '22","sponsor":["SIGMETRICS ACM Special Interest Group on Measurement and Evaluation","SIGCOMM ACM Special Interest Group on Data Communication","SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems","SIGBED ACM Special Interest Group on Embedded Systems","SIGARCH ACM Special Interest Group on Computer Architecture"]},"container-title":["Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3560905.3568304","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3560905.3568304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:10Z","timestamp":1750182550000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3560905.3568304"}},"subtitle":["Causal Intervention with Physical Confounder from IoT Sensor Data for Robust Occupant Information Inference"],"short-title":[],"issued":{"date-parts":[[2022,11,6]]},"references-count":26,"alternative-id":["10.1145\/3560905.3568304","10.1145\/3560905"],"URL":"https:\/\/doi.org\/10.1145\/3560905.3568304","relation":{},"subject":[],"published":{"date-parts":[[2022,11,6]]},"assertion":[{"value":"2023-01-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}