# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "tmle3shift" in publications use:' type: software license: GPL-3.0-only title: 'tmle3shift: Targeted Learning of the Causal Effects of Stochastic Interventions' version: 0.2.2 doi: 10.5281/zenodo.4603372 abstract: Targeted maximum likelihood estimation (TMLE) of population-level causal effects under stochastic treatment regimes and related nonparametric variable importance analyses. Tools are provided for TML estimation of the counterfactual mean under a stochastic intervention characterized as a modified treatment policy, such as treatment policies that shift the natural value of the exposure. The causal parameter and estimation were described in Díaz and van der Laan (2013) and an improved estimation approach was given by Díaz and van der Laan (2018) . authors: - family-names: Hejazi given-names: Nima email: nh@nimahejazi.org orcid: https://orcid.org/0000-0002-7127-2789 - family-names: Coyle given-names: Jeremy email: jeremy.coyle@gmail.com orcid: https://orcid.org/0000-0002-9874-6649 - family-names: Laan given-names: Mark name-particle: van der email: laan@berkeley.edu orcid: https://orcid.org/0000-0003-1432-5511 preferred-citation: type: manual title: 'tmle3shift: Targeted Learning of the Causal Effects of Stochastic Interventions' authors: - family-names: Hejazi given-names: Nima S - family-names: Coyle given-names: Jeremy R - family-names: Laan given-names: Mark J name-particle: van der year: '2024' notes: R package version 0.2.2 doi: 10.5281/zenodo.4603372 url: https://github.com/tlverse/tmle3shift repository: https://tlverse.r-universe.dev repository-code: https://github.com/tlverse/tmle3shift commit: 0c3b8f07d8f5282332fbb822ea12b216f708f7c3 url: https://tlverse.org/tmle3shift contact: - family-names: Hejazi given-names: Nima email: nh@nimahejazi.org orcid: https://orcid.org/0000-0002-7127-2789