{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:43:42Z","timestamp":1743050622051,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434266"},{"type":"electronic","value":"9783031434273"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43427-3_31","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:01:41Z","timestamp":1694898101000},"page":"513-528","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Double Machine Learning at\u00a0Scale to\u00a0Predict Causal Impact of\u00a0Customer Actions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3746-2431","authenticated-orcid":false,"given":"Sushant","family":"More","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6599-359X","authenticated-orcid":false,"given":"Priya","family":"Kotwal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3310-6067","authenticated-orcid":false,"given":"Sujith","family":"Chappidi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2984-859X","authenticated-orcid":false,"given":"Dinesh","family":"Mandalapu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5283-9391","authenticated-orcid":false,"given":"Chris","family":"Khawand","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"issue":"1","key":"31_CR1","doi-asserted-by":"publisher","first-page":"C1","DOI":"10.1111\/ectj.12097","volume":"21","author":"V Chernozhukov","year":"2018","unstructured":"Chernozhukov, V., et al.: Double\/debiased machine learning for treatment and structural parameters. Economet. J. 21(1), C1\u2013C68 (2018). https:\/\/doi.org\/10.1111\/ectj.12097","journal-title":"Economet. J."},{"key":"31_CR2","unstructured":"Sekhon, J.: The Neyman-Rubin Model of Causal Inference and Estimation via Matching Methods. The Oxford Handbook of Political Methodology (2007)"},{"issue":"396","key":"31_CR3","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1080\/01621459.1986.10478354","volume":"81","author":"PW Holland","year":"1986","unstructured":"Holland, P.W.: Statistics and causal inference. J. Am. Stat. Assoc. 81(396), 945\u2013960 (1986). https:\/\/doi.org\/10.1080\/01621459.1986.10478354","journal-title":"J. Am. Stat. Assoc."},{"key":"31_CR4","first-page":"463","volume":"5","author":"J Neyman","year":"1923","unstructured":"Neyman, J.: Sur les applications de la theorie des probabilites aux experiences agricoles: essai des principes. Master\u2019s thesis, excerpts reprinted in English. Stat. Sci. 5, 463\u2013472 (1923)","journal-title":"Stat. Sci."},{"issue":"396","key":"31_CR5","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1080\/01621459.1986.10478354","volume":"81","author":"D Rubin","year":"2005","unstructured":"Rubin, D.: Causal inference using potential outcomes. J. Am. Stat. Assoc. 81(396), 945\u2013960 (2005). https:\/\/doi.org\/10.1080\/01621459.1986.10478354","journal-title":"J. Am. Stat. Assoc."},{"key":"31_CR6","unstructured":"Chernozhukov, V., Goldman, M., Semenova, V., Taddy, M.: Orthogonal Machine Learning for Demand Estimation: High Dimensional Causal Inference in Dynamic Panels. ArXiv:1712.09988 [Stat] (2017)"},{"key":"31_CR7","unstructured":"Huber, P.J.: The behavior of maximum likelihood estimates under nonstandard conditions. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 5, pp. 221\u2013233 (1967)"},{"issue":"4","key":"31_CR8","doi-asserted-by":"publisher","first-page":"817","DOI":"10.2307\/1912934","volume":"48","author":"H White","year":"1980","unstructured":"White, H.: A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48(4), 817\u2013838 (1980)","journal-title":"Econometrica"},{"issue":"260","key":"31_CR9","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1080\/01621459.1952.10483446","volume":"47","author":"DG Horvitz","year":"1952","unstructured":"Horvitz, D.G., Thompson, D.J.: A generalization of sampling without replacement from a Finite Universe. J. Am. Stat. Assoc. 47(260), 663\u2013685 (1952)","journal-title":"J. Am. Stat. Assoc."},{"key":"31_CR10","unstructured":"Nie, X., Wager, S.: Quasi-oracle estimation of heterogeneous treatment effects. ArXiv:1712.04912 [Econ, Math, Stat] (2017)"},{"key":"31_CR11","unstructured":"Kennedy, E.H.: Optimal doubly robust estimation of heterogeneous causal effects. ArXiv:2004.14497 [math.ST] (2020)"},{"key":"31_CR12","doi-asserted-by":"publisher","first-page":"479","DOI":"10.2307\/2532304","volume":"48","author":"JM Robins","year":"1992","unstructured":"Robins, J.M., Mark, S.D.: Estimating exposure effects by modelling the expectation of exposure conditional on confounders. Biometrics 48, 479\u2013495 (1992). MR1173493","journal-title":"Biometrics"},{"key":"31_CR13","volume-title":"Long-Term Outcomes Of Manitoba\u2019s Insight Mentoring Program: A Comparative Statistical Analysis","author":"C Ruth","year":"2015","unstructured":"Ruth, C., et al.: Long-Term Outcomes Of Manitoba\u2019s Insight Mentoring Program: A Comparative Statistical Analysis. Manitoba Centre for Health Policy, Winnipeg, MB (2015)"},{"key":"31_CR14","doi-asserted-by":"publisher","first-page":"3661","DOI":"10.1002\/sim.6607","volume":"34","author":"PC Austin","year":"2015","unstructured":"Austin, P.C., Stuart, E.A.: Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat. Med. 34, 3661\u20133679 (2015)","journal-title":"Stat. Med."},{"key":"31_CR15","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1023\/A:1020371312283","volume":"2","author":"K Hirano","year":"2001","unstructured":"Hirano, K., Imbens, G.W.: Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization. Health Serv. Outcomes Res. Method. 2, 259\u2013278 (2001)","journal-title":"Health Serv. Outcomes Res. Method."},{"issue":"6","key":"31_CR16","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.3982\/ECTA6474","volume":"76","author":"A Abadie","year":"2008","unstructured":"Abadie, A., Imbens, G.: On the failure of bootstrap for matching estimators. Econometrica 76(6), 1537\u20131157 (2008)","journal-title":"Econometrica"},{"key":"31_CR17","unstructured":"Victor, C., Demirer, M., Duflo, E., Fernandez-Val, I.: Generic machine learning inference on heterogenous treatment effects in randomized experiments. aXiv:1712.04802v6 [stat.ML] (2022)"},{"issue":"1","key":"31_CR18","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1093\/ectj\/utaa014","volume":"24","author":"MC Knaus","year":"2021","unstructured":"Knaus, M.C., Lechner, M., Strittmatter, A.: Machine learning estimation of heterogeneous causal effects: empirical monte carlo evidence. Economet. J. 24(1), 134\u2013161 (2021)","journal-title":"Economet. J."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43427-3_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:05:11Z","timestamp":1694898311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43427-3_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434266","9783031434273"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43427-3_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The data presented in the paper is completely anonymized. It cannot be used for inference of personal information of any kind.The method presented in this paper falls in the domain of observational causal inference. Observational causal inference methods are used to gauge impact of things already happened. The inference methods by itself do not aid in any wrong doing. But in the unfortunate case of bad things happening to an individual (e.g., unfair economic policy\/ smoking\/ abuse), the causal methods can help identify the impact and help guide the recovery methods. In that sense, work presented here can be used to seek justice for the victim.Of course, as a society we want to make sure that we do not subject individuals to an unscrupulous treatment to extract the causal impact of that treatment. Because the impact of such treatment could be adverse in some cases. But again the work presented here is used to analyze the aftermath of an action\/ treatment. The type of treatments a person can be subjected to is outside the scope of current work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Implication"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"196","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.63","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}