Datasets › IHDP

IHDP (Infant Health and Development Program)

Introduced by Uri Shalit et al. in Estimating individual treatment effect: generalization bounds and algorithms13 Jun 2016 archive 2025-07-28

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.

Homepage Benchmarks Papers JSON

Benchmarks archive 2025-07-28

All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

		First row (archive order)	Paper	Code
Causal Inference	IHDP	SIP + BCAUSS Average Treatment Effect Error 0.13	Preventing Spurious Interactions: A New Inductive Bias...	RogerG2/NNSIP	13	Compare
Heterogeneous Treatment Effect Estimation	IHDP	CausalPFN PEHE 0.58	CausalPFN: Amortized Causal Effect Estimation via...	vdblm/CausalPFN	2	Compare

Papers archive 2025-07-28

8 shown of 8 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 189. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

			Date	Samples run Syntology
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning	1	1	9 Jun 2025	ran 1 of 1 samples (0 unverified)
Preventing Spurious Interactions: A New Inductive Bias for Accurate Treatment Effect Estimation	1	1	28 May 2025	not harvested
Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation	1	1	20 Mar 2023	not harvested
Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision	0	1	20 Sep 2021	not harvested
Deep representation learning for individualized treatment effect estimation using electronic health records	1	1	1 Dec 2019	not harvested
Adapting Neural Networks for the Estimation of Treatment Effects	5	1	5 Jun 2019	ran 0 of 5 samples (5 unverified; 3 pointer-only for licence)
Causal Effect Inference with Deep Latent-Variable Models	6	1	24 May 2017	not harvested
Estimating individual treatment effect: generalization bounds and algorithms	4	8	13 Jun 2016	ran 0 of 5 samples (5 unverified)

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

Causal Inference Heterogeneous Treatment Effect Estimation

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

IHDP

1 variant name, as the archive lists them.

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