plot.CausalANOVA

An output from <code>CausalANOVA</code>

Factor names to plot. Length should be 2.

fac.name

Which factor serves as the main treatment. Should be 1 (the first element of <code>fac.name</code>) or 2 (the second element of <code>fac.name</code>).

treat.ind

What types of effects to plot. Should be one of <code>AME</code>, <code>AMIE</code> and <code>ConditionalEffect</code>.

type

Space on the left side of the plot.

space

xlim

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.

plot.CausalANOVA: Plotting CausalANOVA

Description

Usage

Arguments