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

## TL;DR

This paper provides practical guidance on using weights in survey experiments, showing that unweighted estimates often suffice for population effects, while weighted methods are better for precise population estimates.

## Contribution

It offers a Neyman-Rubin model-based framework and empirical evidence for when to use weights versus unweighted estimators in survey experiments.

## Key findings

- Unweighted sample estimates often match weighted ones for population effects.
- Weighting can reduce statistical power and may be unnecessary with high-quality samples.
- Post-stratification on weights or covariates improves estimates when precise population effects are needed.

## Abstract

The popularity of online surveys has increased the prominence of using weights that capture units' probabilities of inclusion for claims of representativeness. Yet, much uncertainty remains regarding how these weights should be employed in the analysis of survey experiments: Should they be used or ignored? If they are used, which estimators are preferred? We offer practical advice, rooted in the Neyman-Rubin model, for researchers producing and working with survey experimental data. We examine simple, efficient estimators for analyzing these data, and give formulae for their biases and variances. We provide simulations that examine these estimators as well as real examples from experiments administered online through YouGov. We find that for examining the existence of population treatment effects using high-quality, broadly representative samples recruited by top online survey firms, sample quantities, which do not rely on weights, are often sufficient. We found that Sample Average Treatment Effect (SATE) estimates did not appear to differ substantially from their weighted counterparts, and they avoided the substantial loss of statistical power that accompanies weighting. When precise estimates of Population Average Treatment Effects (PATE) are essential, we analytically show post-stratifying on survey weights and/or covariates highly correlated with the outcome to be a conservative choice. While we show these substantial gains in simulations, we find limited evidence of them in practice.