explain_forest: Explain the results from CEA forests using best linear...

In bonander/CEAforests: Tree-based heterogenous cost-effectiveness and policy learning with individual-level data (beta)

Description

explain_forest Fits a linear regression to the estimated heterogeneous effects to assess the determinants of heterogeneity. Uses augmented inverse probability weighting (AIPW) to de-bias the scores.

Usage

explain_forest(
  forest,
  X = NULL,
  alpha = 0.05,
  WTP = NULL,
  robust.se = TRUE,
  subset = NULL
)

Arguments

forest	A trained CEA forest.
X	The variables to include in the model. If NULL, and intercept only model is run, which gives the ATE.
alpha	The desired significance level, defaults to 0.05.
WTP	The WTP for the net monetary benefit forest. Uses the WTP supplied to the CEA forest if NULL.
robust.se	If robust (sandwich) standard errors are desired. Defaults to TRUE.
subset	A specified subset of the data to compute the regression on.

Value

Returns the results from linear regression(s) on the estimated heterogeneous effects with supplied covariates with asymptotically valid standard errors.

References

Chernozhukov, Victor, and Vira Semenova. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions." arXiv preprint arXiv:1702.06240 (2017).

Examples

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bonander/CEAforests documentation built on April 1, 2021, 10:57 a.m.