predict.FindIt

An output object from <code>FindIt</code>.

object

An optional data frame in which to look for variables with which to predict. If omitted, the data used in <code>FindIt</code> is used.

newdata

Whether to sort samples according to estimated treatment effects.

sort

When <code>sort=TRUE</code>, whether to sort the output in descending order or not.

decreasing

If <code>unique=TRUE</code>, <code>predict</code> returns estimated treatment effects or predicted outcomes for unique samples.

unique

further arguments passed to or from other methods.

&#8230;

<code>predict.FindIt</code> takes an output from <code>FindIt</code> and returns
 estimated treatment effects when <code>treat.type="single"</code> and
 predicted outcomes for each treatment combination when <code>treat.type="multiple"</code>.

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 the function, CausalANOVA, which estimates
the average marginal interaction effects by a regularized ANOVA as
proposed by Egami and Imai (2016+).

predict.FindIt: Computing predicted values for each sample in the data.

Description

Usage

Arguments

Value

Details

Examples