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  "Title": "Estimation of Marginal Treatment Effects using Local\nInstrumental Variables",
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  "Authors@R": "person(\"Xiang\", \"Zhou\", email = \"xiang_zhou@fas.harvard.edu\",\nrole = c(\"aut\", \"cre\"))",
  "Description": "In the generalized Roy model, the marginal treatment\neffect (MTE) can be used as a building block for constructing\nconventional causal parameters such as the average treatment\neffect (ATE) and the average treatment effect on the treated\n(ATT). Given a treatment selection equation and an outcome\nequation, the function mte() estimates the MTE via the\nsemiparametric local instrumental variables method or the\nnormal selection model. The function mte_at() evaluates MTE at\ndifferent values of the latent resistance u with a given X = x,\nand the function mte_tilde_at() evaluates MTE projected onto\nthe estimated propensity score. The function ace() estimates\npopulation-level average causal effects such as ATE, ATT, or\nthe marginal policy relevant treatment effect.",
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  "Repository": "https://xiangzhou09.r-universe.dev",
  "Date/Publication": "2020-06-26 15:22:17 UTC",
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