FindIt: Finding Heterogeneous Treatment Effects
The heterogeneous treatment effect estimation procedure proposed by Imai and Ratkovic (2013). 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 the function, INT, which estimates the average marginal treatment effect, the average treatment combination effect, and the average marginal treatment interaction effect proposed by Egami and Imai (2015).
- Naoki Egami <email@example.com>, Marc Ratkovic <firstname.lastname@example.org>, Kosuke Imai <email@example.com>,
- Date of publication
- 2015-02-27 12:11:22
- Naoki Egami <firstname.lastname@example.org>
- GPL (>= 2)
- FindIt for Estimating Heterogeneous Treatment Effects
- Generating the full factorial design matrix for a FindIt...
- Data from the 1998 New Haven Get-Out-the-Vote Experiment
- Data from conjoint analysis in Hainmueller and Hopkins (2014)...
- Estimating the AMTE, the ATCE and the AMTIE
- National Supported Work Study Experimental Data
- Constructing named all possible interactions from a given set...
- Constructing a named matrix of all two-way interactions.
- Plot estimated treatment effects or predicted outcomes for...
- Computing predicted values for each sample in the data.
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