Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao
arXiv 12 Oct 2022 · Machine Learning · 3 citations (OpenAlex)
arXiv:2210.06594 · PDF · DOI · OpenAlex · Extracted main text
Treatment effect estimation is a fundamental problem in causal inference. We focus on designing efficient randomized controlled trials, to accurately estimate the effect of some treatment on a population of $n$ individuals. In particular, we study sample-constrained treatment effect estimation, where we must select a subset of $s \ll n$ individuals from the population to experiment on. This subset must be further partitioned into treatment and control groups. Algorithms for partitioning the entire population into treatment and control groups, or for choosing a single representative subset, have been well-studied. The key challenge in our setting is jointly choosing a representative subset and a partition for that set. We focus on both individual and average treatment effect estimation, under a linear effects model. We give provably efficient experimental designs and corresponding estimators, by identifying connections to discrepancy minimization and leverage-score-based sampling used in randomized numerical linear algebra. Our theoretical results obtain a smooth transition to known guarantees when $s$ equals the population size. We also empirically demonstrate the performance of our algorithms.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Christopher Harshaw, Fredrik Sävje, Daniel Spielman, and Peng Zhang (2019) Balancing covariates in randomized experiments using the gram-schmidt walk | 0.928 | 10 | 3 | 80% |
| 2 | David P Woodruff (2014) Sketching as a tool for numerical linear algebra | 0.843 | 4 | 3 | 75% |
| 3 | Uri Shalit, Fredrik D Johansson, and David Sontag (2017) Estimating individual treatment effect: generalization bounds and algorithms | 0.737 | 3 | 2 | 100% |
| 4 | Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Z… (2017) Causal effect inference with deep latent-variable models | 0.644 | 3 | 2 | 67% |
| 5 | Xue Chen and Eric Price (2019) Active regression via linear-sample sparsification | 0.644 | 2 | 2 | 100% |
| 6 | David Arbour, Drew Dimmery, and Anup Rao (2021) Efficient balanced treatment assignments for experimentation self | 0.644 | 2 | 2 | 100% |
| 7 | Nathan Kallus (2017) Optimal a priori balance in the design of controlled experiments | 0.585 | 3 | 1 | 100% |
| 8 | Kari Lock Morgan and Donald B Rubin (2012) Rerandomization to improve covariate balance in experiments | 0.585 | 3 | 1 | 100% |
| 9 | Tamas Sarlos (2006) Improved approximation algorithms for large matrices via random projections | 0.585 | 3 | 1 | 100% |
| 10 | Douglas Almond, Kenneth Y Chay, and David S Lee (2005) The costs of low birth weight | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 51 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Limits of Approximating the Median Treatment Effect | 0.405 | 1 | 1 |