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Causal clustering: design of cluster experiments under network interference
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This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global effect. We show that optimal clustering solves a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes simple conditions to choose between any two cluster designs, including choosing between a cluster or individual-level randomization. We illustrate the method's properties using unique network data from the universe of Facebook's users and existing data from a field experiment.
Forward citations
Cited by 5 Pith papers
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Robust Signal Maximization in Spillover Experiments
Under a known linear exposure model, the minimax-optimal spillover experiment correlates treatment shocks by exposure similarity and estimates with a partially whitened, recentered instrumental variable.
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Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
A surrogate for the ATE estimator's MSE is optimized with spectral graph cuts to produce cluster-randomized designs that adapt to the spatial covariance and accommodate moderate-to-large interference.
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GAUGER: Generalized Regression Adjustment via Graph-Weighted Exposure-Level Residualization for Design-Based Inference Under Interference
GAUGER calibrates outcome predictions against the design-induced graph-weighted variance structure to yield a variance-optimal AIPW estimator under network interference.
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Estimation of Treatment Effects Under Nonstationarity via the Truncated Policy Gradient Estimator
A new estimator that replaces immediate outcomes with short-horizon outcome sums can estimate treatment effects with lower bias and variance in nonstationary dynamic systems.
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A Two-armed Bandit Framework for A/B Testing
A two-armed bandit based test statistic with permutation aggregation improves power for A/B testing in both i.i.d. and dynamic settings.
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