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LARF: Instrumental Variable Estimation of Causal Effects through Local Average Response Functions

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  • An, Weihua
  • Wang, Xuefu

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.

Suggested Citation

  • An, Weihua & Wang, Xuefu, 2016. "LARF: Instrumental Variable Estimation of Causal Effects through Local Average Response Functions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(c01).
    • Handle: RePEc:jss:jstsof:v:071:c01
    DOI: http://hdl.handle.net/10.18637/jss.v071.c01
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    References listed on IDEAS

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    7. Donald B. Rubin, 1977. "Assignment to Treatment Group on the Basis of a Covariate," Journal of Educational and Behavioral Statistics, , vol. 2(1), pages 1-26, March.
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    3. Yao, Yuxin & Zhang, Yi, 2023. "The long-term and intergenerational effects of early-life hunger experience on human capital and labor market outcomes," China Economic Review, Elsevier, vol. 77(C).
    4. Hsu Yu-Chin & Huber Martin & Lai Tsung-Chih, 2019. "Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-20, January.
    5. Bach, Ruben L. & Eckman, Stephanie, 2017. "Does participating in a panel survey change respondents' labor market behavior?," IAB-Discussion Paper 201715, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    6. Rossmann, Tobias, 2019. "Does Experience Shape Subjective Expectations?," Rationality and Competition Discussion Paper Series 181, CRC TRR 190 Rationality and Competition.
    7. Lee, Wang-Sheng & Li, Ben G., 2021. "Extreme weather and mortality: Evidence from two millennia of Chinese elites," Journal of Health Economics, Elsevier, vol. 76(C).
    8. Weihua An & Ying Ding, 2018. "The Landscape of Causal Inference: Perspective From Citation Network Analysis," The American Statistician, Taylor & Francis Journals, vol. 72(3), pages 265-277, July.

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