Infant Health and Development Program
The Infant Health and Development Program (IHDP) is a randomized controlled study designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The datasets is first used for benchmarking treatment effect estimation algorithms in Hill [35], where selection bias is induced by removing non-random subsets of the treated individuals to create an observational dataset, and the outcomes are generated using the original covariates and treatments. It contains 747 subjects and 25 variables.
Variants: IHDP
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Causal Inference | Dragonnet | Adapting Neural Networks for the … | 2019-06-05 |
| Causal Inference | CEVAE | Causal Effect Inference with Deep … | 2017-05-24 |
| Causal Inference | Causal Forest | Estimating individual treatment effect: generalization … | 2016-06-13 |
| Causal Inference | Balancing Neural Network | Estimating individual treatment effect: generalization … | 2016-06-13 |
| Causal Inference | k-NN | Estimating individual treatment effect: generalization … | 2016-06-13 |
| Causal Inference | Balancing Linear Regression | Estimating individual treatment effect: generalization … | 2016-06-13 |
| Causal Inference | Counterfactual Regression + WASS | Estimating individual treatment effect: generalization … | 2016-06-13 |
| Causal Inference | Random Forest | Estimating individual treatment effect: generalization … | 2016-06-13 |
| Causal Inference | TARNet | Estimating individual treatment effect: generalization … | 2016-06-13 |
Recent papers with results on this dataset: