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Causal inference in network experiments: regression-based analysis and design-based properties
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Network experiments are powerful tools for studying spillover effects, which avoid endogeneity by randomly assigning treatments to units over networks. However, it is non-trivial to analyze network experiments properly without imposing strong modeling assumptions. We show that regression-based point estimators and standard errors can have strong theoretical guarantees if the regression functions and robust standard errors are carefully specified to accommodate the interference patterns under network experiments. We first recall a well-known result that the H\'ajek estimator is numerically identical to the coefficient from the weighted-least-squares fit based on the inverse probability of the exposure mapping. Moreover, we demonstrate that the regression-based approach offers three notable advantages: its ease of implementation, the ability to derive standard errors through the same regression fit, and the potential to integrate covariates into the analysis to improve efficiency. Recognizing that the regression-based network-robust covariance estimator can be anti-conservative under nonconstant effects, we propose an adjusted covariance estimator to improve the empirical coverage rates.
Forward citations
Cited by 8 Pith papers
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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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Causal Inference under Interference: Regression Adjustment and Optimality
Under network interference, linear and kernel regression adjustments achieve the smallest asymptotic variance in their classes, and the paper provides consistent variance estimators.
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Design-based causal inference in bipartite experiments
A Hájek estimator for the total treatment effect in bipartite experiments is shown consistent and asymptotically normal under sparse graph assumptions, with a conservative variance estimator and covariate adjustment.
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Endogenous Interference in Randomized Experiments
A framework with asymptotic theory separating direct treatment effects from network-mediated indirect effects in randomized experiments where the social network responds to the treatment.
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Adjusting auxiliary variables under approximate neighborhood interference
Introduces a regression-adjustment estimator for network experiments under approximate neighborhood interference that is proven asymptotically no less efficient than an unadjusted estimator.
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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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Design-Based and Network Sampling-Based Uncertainties in Network Experiments
Correlations among exposure-mapping regressors make OLS spillover coefficients weighted sums of heterogeneous effects plus contamination terms, and sampled networks can create such correlations even when the populatio...
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Online Experimental Design With Estimation-Regret Trade-off Under Network Interference
A Pareto-optimal trade-off between regret and treatment-effect estimation is derived and achieved for bandits with network interference, by compressing the action space through exposure mapping.
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