Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
LARF: Instrumental Variable Estimation of Causal Effects through Local Average Response Functions | An | Journal of Statistical Software
Authors: Weihua An, Xuefu Wang
Title: LARF: Instrumental Variable Estimation of Causal Effects through Local Average Response Functions
Abstract: LARF is an R package that provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument (i.e., the treatment inducement) are binary. The method (Abadie 2003) involves two steps. First, pseudo-weights are constructed from the probability of receiving the treatment inducement. By default LARF estimates the probability by a probit regression. It also provides semiparametric power series estimation of the probability and allows users to employ other external methods to estimate the probability. Second, the pseudo-weights are used to estimate the local average response function conditional on treatment and covariates. LARF provides both least squares and maximum likelihood estimates of the conditional treatment effects.

Page views:: 1140. Submitted: 2013-04-04. Published: 2016-07-26.
Paper: LARF: Instrumental Variable Estimation of Causal Effects through Local Average Response Functions     Download PDF (Downloads: 1049)
Supplements:
LARF_1.4.tar.gz: R source package Download (Downloads: 61; 144KB)
v71c01.R: R replication code Download (Downloads: 79; 4KB)
v71c01.do: Stata replication code Download (Downloads: 81; 1KB)
DOI: 10.18637/jss.v071.c01

by
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.