# Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shift

arXiv: https://arxiv.org/abs/2608.06512 · HTML: https://mlsanity.com/paper/2608.06512/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals](https://mlsanity.com/paper/1709.04589/) (2017)
- [Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments](https://mlsanity.com/paper/2203.14511/) (2022)
- [Efficient Targeted Learning of Heterogeneous Treatment Effects for Multiple Subgroups](https://mlsanity.com/paper/2211.14671/) (2022)
- [Adaptive Experiments Toward Learning Treatment Effect Heterogeneity](https://mlsanity.com/paper/2312.06883/) (2023)
- [Minimax-Regret Sample Selection in Randomized Experiments](https://mlsanity.com/paper/2403.01386/) (2024)
- [Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching](https://mlsanity.com/paper/1908.04748/) (2019)
- [Designing Transportable Experiments](https://mlsanity.com/paper/2009.03860/) (2020)
- [Strategy to select most efficient RCT samples based on observational data](https://mlsanity.com/paper/2211.04672/) (2022)

## Abstract (arXiv)

Randomized experiments are often run in one population to guide decisions in another. Allocating by experimental proportions wastes budget on groups that rarely appear in deployment, whereas allocating by deployment proportions under-samples groups that are hard to measure precisely. We propose TWNA (Target-Weighted Neyman Allocation), a two-stage stratified design that uses pilot estimates of group--arm outcome variances to allocate final-stage sample sizes and treatment probabilities for target-weighted group average treatment effect (GATE) precision. The oracle rule has a closed form and balances deployment importance with statistical difficulty; the plug-in rule recovers it as pilot variance estimates stabilize. We also extend TWNA to handle uncertainty about deployment composition, remaining robust whether the target mix is roughly known or entirely unknown. Finally, we distinguish this weight robustness from a pilot-robust variant for skewed, rare-event, or contaminated outcomes. Simulations and real-covariate benchmarks show the largest gains when groups are both deployment-important and difficult to measure.