This Element offers a practical guide to estimating conditional marginal effects-how treatment effects vary with a moderating variable-using modern statistical methods. Commonly used approaches, such as linear interaction models, often suffer from unclarified estimands, limited overlap, and restrictive functional forms. This guide begins by clearly defining the estimand and presenting the main identification results. It then reviews and improves upon existing solutions, such as the semiparametric kernel estimator, and introduces robust estimation strategies, including augmented inverse propensity score weighting with Lasso selection (AIPW-Lasso) and double machine learning (DML) with modern algorithms. Each method is evaluated through simulations and empirical examples, with practical recommendations tailored to sample size and research context. All tools are implemented in the accompanying \texttt{interflex} package for \texttt{R}.

翻译：本文提供了一份关于使用现代统计方法估计条件边际效应（即处理效应如何随调节变量变化）的实用指南。常用的方法，如线性交互模型，通常存在估计目标不明确、重叠有限和函数形式限制等问题。本指南首先明确定义了估计目标，并给出了主要的识别结果。随后，它回顾并改进了现有解决方案，例如半参数核估计器，并介绍了稳健的估计策略，包括带有Lasso选择的增强逆倾向得分加权法（AIPW-Lasso）以及结合现代算法的双重机器学习（DML）。每种方法都通过模拟和实证案例进行了评估，并根据样本量和研究背景提供了实用的建议。所有工具均在随附的用于 \texttt{R} 的 \texttt{interflex} 包中实现。