tag:joss.theoj.org,2005:/papers/tagged/semiparametric%20estimationJournal of Open Source Software2023-11-05T15:58:05ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/42122023-11-05T15:58:05Z2023-11-06T00:00:26ZSuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in Racceptedv1.0.02023-02-15 18:37:21 UTC912023-11-05 15:58:05 UTC820235422DavidMcCoyDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0002-5515-6307AlejandroSchulerDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0003-4853-6130AlanHubbardDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0002-3769-0127Markvan der LaanDepartment of Biostatistics, University of California Berkeley, Berkeley, CA 94704, U.S.A.0000-0003-1432-551110.21105/joss.05422https://doi.org/10.5281/zenodo.10038794Rhttps://joss.theoj.org/papers/10.21105/joss.05422.pdfcausal inference, machine learning, stochastic interventions, efficient estimation, targeted learning, mixed exposurestag:joss.theoj.org,2005:Paper/36132022-09-23T14:45:34Z2022-09-25T16:58:01Zhaldensify: Highly adaptive lasso conditional density estimation in Racceptedv0.2.52022-05-18 15:31:04 UTC772022-09-23 14:45:34 UTC720224522NimaS.HejaziDepartment of Biostatistics, T.H. Chan School of Public Health, Harvard University0000-0002-7127-2789MarkJ.van der LaanDivision of Biostatistics, School of Public Health, University of California, Berkeley, Department of Statistics, University of California, Berkeley0000-0002-1019-8343DavidBenkeserDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University0000-0002-1019-834310.21105/joss.04522https://doi.org/10.5281/zenodo.7089147Rhttps://joss.theoj.org/papers/10.21105/joss.04522.pdfmachine learning, causal inference, conditional density estimation, generalized propensity score, inverse probability weighting, semiparametric inferencetag:joss.theoj.org,2005:Paper/31652022-01-05T20:07:56Z2023-10-14T09:29:53Zmedoutcon: Nonparametric efficient causal mediation analysis with machine learning in Racceptedv0.1.52021-11-10 22:10:23 UTC692022-01-05 20:07:56 UTC720223979NimaS.HejaziDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, USA0000-0002-7127-2789KaraE.RudolphDepartment of Epidemiology, Mailman School of Public Health, Columbia University, USA0000-0002-9417-7960IvánDíazDivision of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, USA0000-0001-9056-204710.21105/joss.03979https://doi.org/10.5281/zenodo.5809520Rhttps://joss.theoj.org/papers/10.21105/joss.03979.pdfcausal inference, machine learning, semiparametric estimation, mediation analysis, natural direct effect, interventional direct effect