Design and analysis of bipartite experiments under a linear exposure-response model
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Publication:2681751
DOI10.1214/23-EJS2111MaRDI QIDQ2681751
Fredrik Sävje, Jean Pouget-Abadie, Christopher Harshaw, David Eisenstat, Vahab S. Mirrokni
Publication date: 6 February 2023
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2103.06392
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- Identification of peer effects through social networks
- Fundamentals of Stein's method
- Rerandomization to improve covariate balance in experiments
- Correlation clustering
- Estimating average causal effects under general interference, with application to a social network experiment
- Distributed balanced partitioning via linear embedding
- Bipartite causal inference with interference
- Average treatment effects in the presence of unknown interference
- Clustering with qualitative information
- Identification of Endogenous Social Effects: The Reflection Problem
- Toward Causal Inference With Interference
- Statistics and Causal Inference
- Root-N-Consistent Semiparametric Regression
- Approximating Matrix Multiplication for Pattern Recognition Tasks
- Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants
- Asymptotic theory of rerandomization in treatment–control experiments
- Adjusting Treatment Effect Estimates by Post-Stratification in Randomized Experiments
- Causal Inference for Statistics, Social, and Biomedical Sciences
- Applying the Horvitz-Thompson criterion in complex designs: A computer-intensive perspective for estimating inclusion probabilities
- A Generalization of Sampling Without Replacement From a Finite Universe
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