Package: FindIt 1.2.0

FindIt: Finding Heterogeneous Treatment Effects

The heterogeneous treatment effect estimation procedure proposed by Imai and Ratkovic (2013)<doi:10.1214/12-AOAS593>. The proposed method is applicable, for example, when selecting a small number of most (or least) efficacious treatments from a large number of alternative treatments as well as when identifying subsets of the population who benefit (or are harmed by) a treatment of interest. The method adapts the Support Vector Machine classifier by placing separate LASSO constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. This allows for the qualitative distinction between causal and other parameters, thereby making the variable selection suitable for the exploration of causal heterogeneity. The package also contains a class of functions, CausalANOVA, which estimates the average marginal interaction effects (AMIEs) by a regularized ANOVA as proposed by Egami and Imai (2019)<doi:10.1080/01621459.2018.1476246>. It contains a variety of regularization techniques to facilitate analysis of large factorial experiments.

Authors:Naoki Egami, Marc Ratkovic, Kosuke Imai

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FindIt/json (API)

# Install 'FindIt' in R:
install.packages('FindIt', repos = c('https://naoki-egami.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • Carlson - Data from conjoint analysis in Carlson (2015).
  • GerberGreen - Data from the 1998 New Haven Get-Out-the-Vote Experiment
  • LaLonde - National Supported Work Study Experimental Data

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5 exports 1.51 score 36 dependencies 5 mentions 8 scripts 285 downloads

Last updated 5 years agofrom:96869e3ea4. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 04 2024
R-4.5-winOKSep 04 2024
R-4.5-linuxOKSep 04 2024
R-4.4-winOKSep 04 2024
R-4.4-macOKSep 04 2024
R-4.3-winOKSep 04 2024
R-4.3-macOKSep 04 2024

Exports:CausalANOVAConditionalEffectcv.CausalANOVAFindIttest.CausalANOVA

Dependencies:abindarmbootclicodacodetoolscpp11foreachglinternetglmnetglueigraphiteratorslarslatticelifecyclelimSolvelme4lmtestlpSolvemagrittrMASSMatrixminqanlmenloptrpkgconfigquadprogRcppRcppEigenrlangsandwichshapesurvivalvctrszoo

Readme and manuals

Help Manual

Help pageTopics
FindIt: Finding Heterogeneous Treatment EffectsFindIt-package CausalANOVAFit cluster_se_glm CoefExtract Collapsing CreateANOVAconst CreatelevelIndex CreateWeights FullVCOV glinternet.cv Glsei indTwo2Three Lcombinefunction lengthSlack makeallway maketwoway NoRegularization PsyConstraintCombine rangeCausalANOVAFit ScreenINT stab.CausalANOVA Zcombinefunction
Data from conjoint analysis in Carlson (2015).Carlson
Estimating the AMEs and AMIEs with the CausalANOVA.CausalANOVA
Estimating the Conditional Effects with the CausalANOVA.ConditionalEffect
Cross validation for the CausalANOVA.cv.CausalANOVA plot.cv.CausalANOVA
FindIt for Estimating Heterogeneous Treatment EffectsFindIt
Data from the 1998 New Haven Get-Out-the-Vote ExperimentGerberGreen
National Supported Work Study Experimental DataLaLonde
Plotting CausalANOVAplot.CausalANOVA
Plot estimated treatment effects or predicted outcomes for each treatment combination.plot.PredictFindIt
Computing predicted values for each sample in the data.predict.FindIt
Summarizing CausalANOVA outputsummary.CausalANOVA
Summarizing FindIt outputsummary.FindIt
Estimating the AMEs and AMIEs after Regularization with the CausalANOVA.test.CausalANOVA