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The popularity of online surveys has increased the prominence of usingweights that capture units' probabilities of inclusion for claims ofrepresentativeness. Yet, much uncertainty remains regarding how these weightsshould be employed in the analysis of survey experiments: Should they be usedor ignored? If they are used, which estimators are preferred? We offerpractical advice, rooted in the Neyman-Rubin model, for researchers producingand working with survey experimental data. We examine simple, efficientestimators for analyzing these data, and give formulae for their biases andvariances. We provide simulations that examine these estimators as well as realexamples from experiments administered online through YouGov. We find that forexamining the existence of population treatment effects using high-quality,broadly representative samples recruited by top online survey firms, samplequantities, which do not rely on weights, are often sufficient. We found thatSample Average Treatment Effect (SATE) estimates did not appear to differsubstantially from their weighted counterparts, and they avoided thesubstantial loss of statistical power that accompanies weighting. When preciseestimates of Population Average Treatment Effects (PATE) are essential, weanalytically show post-stratifying on survey weights and/or covariates highlycorrelated with the outcome to be a conservative choice. While we show thesesubstantial gains in simulations, we find limited evidence of them in practice.

Worth Weighting? How to Think About and Use Weights in Survey Experiments