Year: 2022
Type: conference-paper
Abstract: 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.
DOI: 10.52202/068431-0391
Authors: Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: RI: Medium: Extreme Clustering, AF: Small: Collaborative Research: New Challenges in Graph Stream Algorithms and Related Communication Games, HDR TRIPODS: Institute for Integrated Data Science: A Transdisciplinary Approach to Understanding Fundamental Trade-offs and Theoretical Foundations, CAREER: Fast Linear Algebra: Algorithms and Fundamental Limits, AitF: Efficient Memory Management via Randomized, Streaming, and Online Algorithms
OpenAlex ID: W7133228406