{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T01:52:49Z","timestamp":1756000369690,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684482"},{"type":"electronic","value":"9781643684499"}],"license":[{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,19]]},"abstract":"<jats:p>Neural network-based treatment effect estimation algorithms are well-known in the causal inference community. Many works propose new designs and architectures and report performance metrics over benchmarking data sets, in a Machine Learning manner. Nevertheless, most authors focus solely on binary treatment scenarios. This is a limitation, as many real-world scenarios have a multivalued treatment. In this work, we present a novel approach where we generalize a top-performing, neural network-based algorithm for binary treatment effect estimation to a multi-valued treatment setting. Our approach yields an estimator with desirable asymptotic properties, that delivers very good results in a wide range of experiments. To the best of our knowledge, this work is opening ground for the benchmarking of neural network-based algorithms for multi-valued treatment effect estimation.<\/jats:p>","DOI":"10.3233\/faia230655","type":"book-chapter","created":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T08:14:08Z","timestamp":1698048848000},"source":"Crossref","is-referenced-by-count":2,"title":["Hydranet: A Neural Network for the Estimation of Multi-Valued Treatment Effects"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4718-3388","authenticated-orcid":false,"given":"Borja","family":"Velasco-Regulez","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Institute (IIIA-CSIC)"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3752-644X","authenticated-orcid":false,"given":"Jesus","family":"Cerquides","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute (IIIA-CSIC)"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230655","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T08:14:09Z","timestamp":1698048849000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230655"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,19]]},"ISBN":["9781643684482","9781643684499"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230655","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2023,10,19]]}}}