Package: localIV 0.3.1

localIV: Estimation of Marginal Treatment Effects using Local Instrumental Variables

In the generalized Roy model, the marginal treatment effect (MTE) can be used as a building block for constructing conventional causal parameters such as the average treatment effect (ATE) and the average treatment effect on the treated (ATT). Given a treatment selection equation and an outcome equation, the function mte() estimates the MTE via the semiparametric local instrumental variables method or the normal selection model. The function mte_at() evaluates MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at() evaluates MTE projected onto the estimated propensity score. The function ace() estimates population-level average causal effects such as ATE, ATT, or the marginal policy relevant treatment effect.

Authors:Xiang Zhou [aut, cre]

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Bug tracker:https://github.com/xiangzhou09/localiv/issues

On CRAN:

6 exports 5 stars 1.10 score 71 dependencies 6 scripts 191 downloads

Last updated 4 years agofrom:23d308ab0b. Checks:OK: 3 NOTE: 4. Indexed: yes.

Exports:acemtemte_atmte_localIVmte_normalmte_tilde_at

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdigestdoBydplyrfansifarverFormulagenericsggplot2gluegtableisobandKernSmoothlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmaxLikmgcvmicrobenchmarkminqamiscToolsmodelrmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6RColorBrewerRcppRcppEigenrlangsampleSelectionsandwichscalesSparseMstringistringrsurvivalsystemfittibbletidyrtidyselectutf8vctrsVGAMviridisLitewithrzoo