{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-adaptive-kernel-approach-to-federated","title":"An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects","arxiv_id":"2301.00346","date":"2023-01-01","proceeding":null,"authors":["Thanh Vinh Vo","Arnab Bhattacharyya","Young Lee","Tze-Yun Leong"],"abstract":"We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. The heterogeneous causal effects can be estimated with no sharing of the raw training data among the sources, thus minimizing the risk of privacy leak. We also provide minimax lower bounds to assess the quality of the parameters learned from the disparate sources. The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.","url_abs":"https://arxiv.org/abs/2301.00346v1","url_pdf":"https://arxiv.org/pdf/2301.00346v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-adaptive-kernel-approach-to-federated","repo_url":"https://github.com/vothanhvinh/causalrff","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"federated-learning","task_name":"Federated Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2301.00346","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.00346"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vothanhvinh/causalrff","reach":null}],"summary":{"ran":1,"ran_honours":1,"ran_fixture":1,"unverified":1},"by_repo_kind":{"official":{"samples":4,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":4,"samples":[{"code_sha256_prefix":"feee37b13a78bc70","entry":"ModelZY","repo":"vothanhvinh/causalrff","repo_kind":"official","path":"model.py","file_url":"https://github.com/vothanhvinh/causalrff/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"feee37b13a78bc70"}},{"code_sha256_prefix":"4e933f0a68120808","entry":"draw_spectral_SE","repo":"vothanhvinh/causalrff","repo_kind":"official","path":"model_train.py","file_url":"https://github.com/vothanhvinh/causalrff/blob/HEAD/model_train.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4e933f0a68120808"}},{"code_sha256_prefix":"2d2fd684fc6704be","entry":"testW","repo":"vothanhvinh/causalrff","repo_kind":"official","path":"model_train.py","file_url":"https://github.com/vothanhvinh/causalrff/blob/HEAD/model_train.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2d2fd684fc6704be"}},{"code_sha256_prefix":"fffeacd082faeb32","entry":"trainW","repo":"vothanhvinh/causalrff","repo_kind":"official","path":"model_train.py","file_url":"https://github.com/vothanhvinh/causalrff/blob/HEAD/model_train.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"fffeacd082faeb32"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}