{"doi":"10.1111/ajps.12526","title":"Adjusting for Confounding with Text Matching","abstract":"<jats:title>Abstract</jats:title><jats:p>We identify situations in which conditioning on text can address confounding in observational studies. We argue that a matching approach is particularly well‐suited to this task, but existing matching methods are ill‐equipped to handle high‐dimensional text data. Our proposed solution is to estimate a low‐dimensional summary of the text and condition on this summary via matching. We propose a method of text matching, topical inverse regression matching, that allows the analyst to match both on the topical content of confounding documents and the probability that each of these documents is treated. We validate our approach and illustrate the importance of conditioning on text to address confounding with two applications: the effect of perceptions of author gender on citation counts in the international relations literature and the effects of censorship on Chinese social media users.</jats:p>","journal":"American Journal of Political Science","year":2020,"id":602444,"datarank":0.6814942173405006,"base_score":4.543294782270004,"endowment":4.543294782270004,"self_citation_contribution":0.6814942173405006,"citation_network_contribution":0.0,"self_endowment_contribution":0.6814942173405006,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":93,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1545066,"name":"Brandon M. 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We propose a method of text matching, topical inverse regression matching, that allows the analyst to match both on the topical content of confounding documents and the probability that each of these documents is treated. We validate our approach and illustrate the importance of conditioning on text to address confounding with two applications: the effect of perceptions of author gender on citation counts in the international relations literature and the effects of censorship on Chinese social media users.</jats:p>","is_dataset_classified":null,"base_score":4.543294782270004,"endowment":4.543294782270004,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"23304386","pmcid":null,"openalex_id":"https://openalex.org/W3043997204","authors":[],"funders":[{"funder_name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","grant_id":"P2‐CHD047879","title":null}],"total_grants":1,"fwci":91.5762,"citation_percentile":0.9986366,"influential_citations":0,"citation_trend":[{"year":2018,"count":1},{"year":2019,"count":4},{"year":2020,"count":8},{"year":2021,"count":24},{"year":2022,"count":19},{"year":2023,"count":9},{"year":2024,"count":8},{"year":2025,"count":15},{"year":2026,"count":5}],"oa_status":"closed","license":"http://onlinelibrary.wiley.com/termsAndConditions#vor","oa_locations":[{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fajps.12526","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1111/ajps.12526","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/full-xml/10.1111/ajps.12526","host_type":"publisher"},{"url":"https://doi.org/10.1111/ajps.12526","host_type":"journal"}],"fields_of_study":["Computational and Text Analysis Methods","Gender Politics and Representation","Media Influence and Politics"],"mesh_terms":[],"keywords":["Confounding","Matching (statistics)","Computer science","Observational study","Citation","Bounding overwatch","Information retrieval","Propensity score matching","Task (project management)","Statistics","Artificial intelligence","Mathematics","World Wide Web","Engineering"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Gender equality"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T19:25:02.444069Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}