ihdp: Infant Health and Development Program Data

In bartcs: Bayesian Additive Regression Trees for Confounder Selection

Infant Health and Development Program Data

Description

Infant Health and Development Program (IHDP) is a randomized experiment from 1985 to 1988 which studied the effect of home visits on cognitive test scores for infants.

Usage

ihdp

Format

treatment

Given treatment.

y_factual

Observed outcome.

y_cfactual

Potential outcome given the opposite treatment.

mu0

Control conditional means.

mu1

Treated conditional means.

X1 ~ X6

Confounders with continuous values.

X7 ~ X25

Confounders with binary values.

Details

This dataset was first used by Hill (2011), then used by other researchers (Shalit et al. 2017, Louizos et al. 2017).

Source

Our version of dataset is the dataset used by Louizos et al. (2017). This is the first realization of 10 generated datasets and you can find other realizations from https://github.com/AMLab-Amsterdam/CEVAE.

References

Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1), 217-240. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/jcgs.2010.08162")}

Louizos, C., Shalit, U., Mooij, J. M., Sontag, D., Zemel, R., & Welling, M. (2017). Causal effect inference with deep latent-variable models. Advances in neural information processing systems, 30. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.1705.08821")} https://github.com/AMLab-Amsterdam/CEVAE

Shalit, U., Johansson, F. D., & Sontag, D. (2017, July). Estimating individual treatment effect: generalization bounds and algorithms. In International Conference on Machine Learning (pp. 3076-3085). PMLR. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.1606.03976")}

bartcs documentation built on June 22, 2024, 6:48 p.m.