{"doi":"10.1093/aje/kwac005","title":"RE: “SYNTHETIC CONTROL METHODS FOR THE EVALUATION OF SINGLE-UNIT INTERVENTIONS IN EPIDEMIOLOGY: A TUTORIAL”","abstract":"Editor's note: In accordance with Journal policy, Bonander et al. were asked whether they wished to respond to this letter, but they chose not to do so. We congratulate Bonander et al. (1) for their useful tutorial on the synthetic control (SC) approach. The original SC framework of Abadie et al. (2) offers a potential solution to a challenging problem in causal inference: how to evaluate the effect of a nonrandomized intervention when a single unit is treated. A preintervention versus postintervention comparison within a single unit could be conducted in principle but may be confounded by factors that change over time. One solution is to examine a “control” unit, ideally comparable to the treated unit in all ways except the intervention, which could be examined for evidence of changes in the outcome over time in the absence of treatment. However, a natural control unit may not be available. The SC framework leverages a heterogeneous pool of untreated units to form a “synthetic control,” a weighted average of untreated units built to match the treated unit’s pretreatment outcome trajectory. SC weights are typically estimated by regressing pretreatment outcomes in the treated on those in the untreated. In this letter, we aim to highlight an important consideration missing from the tutorial that may threaten the validity of a standard regression approach for identifying SC weights, irrespective of whether the latter are constrained to be nonnegative and normalized, and to describe a potential solution.","journal":"American Journal of Epidemiology","year":2022,"id":302608,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9631,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":489788,"name":"Oliver Dukes","orcid":"0000-0002-9145-3325","position":1,"is_corresponding":false},{"id":280662,"name":"Xu Shi","orcid":"0000-0001-8566-9552","position":2,"is_corresponding":false},{"id":994539,"name":"Miao Wang","orcid":"0000-0002-4038-8818","position":3,"is_corresponding":false},{"id":344876,"name":"David B. Richardson","orcid":"0000-0001-8550-0212","position":4,"is_corresponding":false},{"id":280665,"name":"Eric J. Tchetgen Tchetgen","orcid":"0000-0002-8369-3900","position":0,"is_corresponding":true}],"reference_count":6,"raw_metadata":null,"created_at":"2026-07-19T00:32:16.279991Z","pmid":"35136907","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":[]}