{"doi":"10.1093/jamia/ocaa346","title":"Deep propensity network using a sparse autoencoder for estimation of treatment effects","abstract":"OBJECTIVE: Drawing causal estimates from observational data is problematic, because datasets often contain underlying bias (eg, discrimination in treatment assignment). To examine causal effects, it is important to evaluate what-if scenarios-the so-called \"counterfactuals.\" We propose a novel deep learning architecture for propensity score matching and counterfactual prediction-the deep propensity network using a sparse autoencoder (DPN-SA)-to tackle the problems of high dimensionality, nonlinear/nonparallel treatment assignment, and residual confounding when estimating treatment effects. MATERIALS AND METHODS: We used 2 randomized prospective datasets, a semisynthetic one with nonlinear/nonparallel treatment selection bias and simulated counterfactual outcomes from the Infant Health and Development Program and a real-world dataset from the LaLonde's employment training program. We compared different configurations of the DPN-SA against logistic regression and LASSO as well as deep counterfactual networks with propensity dropout (DCN-PD). Models' performances were assessed in terms of average treatment effects, mean squared error in precision on effect's heterogeneity, and average treatment effect on the treated, over multiple training/test runs. RESULTS: The DPN-SA outperformed logistic regression and LASSO by 36%-63%, and DCN-PD by 6%-10% across all datasets. All deep learning architectures yielded average treatment effects close to the true ones with low variance. Results were also robust to noise-injection and addition of correlated variables. Code is publicly available at https://github.com/Shantanu48114860/DPN-SAz. DISCUSSION AND CONCLUSION: Deep sparse autoencoders are particularly suited for treatment effect estimation studies using electronic health records because they can handle high-dimensional covariate sets, large sample sizes, and complex heterogeneity in treatment assignments.","journal":"Journal of the American Medical Informatics Association","year":2020,"id":72769,"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":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9477,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":23318,"name":"Jiang Bian","orcid":"0000-0002-2238-5429","position":1,"is_corresponding":false},{"id":263949,"name":"Yi Guo","orcid":"0000-0003-0587-4105","position":2,"is_corresponding":false},{"id":285001,"name":"Mattia Prosperi","orcid":"0000-0002-9021-5595","position":3,"is_corresponding":false},{"id":383400,"name":"Shantanu Ghosh","orcid":"0000-0003-4085-541X","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-18T21:45:09.279825Z","pmid":"33594415","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":[]}