Surveys are an important research tool, providing unique measurements on subjective experiences such as sentiment and opinions that cannot be measured by other means. However, because survey data is collected from a self-selected group of participants, directly inferring insights from it to a population of interest, or training ML models on such data, can lead to erroneous estimates or under-performing models. In this paper we present balance, an open-source Python package by Meta, offering a simple workflow for analyzing and adjusting biased data samples with respect to a population of interest. The balance workflow includes three steps: understanding the initial bias in the data relative to a target we would like to infer, adjusting the data to correct for the bias by producing weights for each unit in the sample based on propensity scores, and evaluating the final biases and the variance inflation after applying the fitted weights. The package provides a simple API that can be used by researchers and data scientists from a wide range of fields on a verity of data. The paper provides the relevant context, methodological background, and presents the package's API.

翻译：调查是一种重要的研究工具，能够提供对情感和观点等主观体验的独特测量，而这些无法通过其他手段获得。然而，由于调查数据来自自我选择的参与者群体，若直接从中推断目标人群的见解或基于此类数据训练机器学习模型，可能导致错误的估计或性能不佳的模型。本文介绍Meta公司开源的Python包——balance，它提供了一个用于分析和调整相对于目标人群的有偏数据样本的简易工作流程。balance的工作流程包括三个步骤：理解数据相对于推断目标的初始偏差，通过基于倾向性得分为样本中每个单元生成权重来校正偏差，以及评估应用拟合权重后的最终偏差和方差膨胀。该包提供了一个简单API，可供广泛领域的研究人员和数据科学家在各种数据上使用。本文提供了相关背景、方法论基础，并介绍了该包的API。