spline_cate: Estimates non-paramteric CATEs using spline regression as...

In MCKnaus/causalDML: Causal Double Machine Learning

Estimates non-paramteric CATEs using spline regression as proposed by Semenova and Chernozhukov (2020) using the crs package.

Description

Estimates non-paramteric CATEs using spline regression as proposed by Semenova and Chernozhukov (2020) using the crs package.

Usage

spline_cate(delta, z, ...)

Arguments

delta	Vector of doubly robust ATE score. E.g obtained as one column of ATE_dml$delta from ATE_dml.
z	Heterogeneity variable(s) vector, matrix or data.frame
...	Pass crs options, otherwise default settings are used

Value

spline_cate object:

model	crs object of the spline regression
fit	Fitted values of the spline regression
se_fit	Robust standard errors of fitted values
ate	Average teratment effect
z	Original heterogeneity variable(s) data.frame

References

• Semenova, V., & Chernozhukov, V. (2020). Debiased machine learning of conditional average treatment effects and other causal functions. The Econometrics Journal, utaa027, doi: https://doi.org/10.1093/ectj/utaa027

MCKnaus/causalDML documentation built on June 11, 2025, 12:30 a.m.