用AI生成图像对分离肤色影响，更准估算广告中视觉属性的因果效应。
Estimating Visual Attribute Effects in Advertising from Observational Data: A Deepfake-Informed Double Machine Learning Approach
- 通过深度伪造生成配对图像，隔离肤色变量并消除背景干扰
- 在23万条网红帖子上验证，深肤色对点赞数有轻微负向影响（-522，p=0.062）
- 适合研究广告视觉效果、数字营销与因果推断的学者和从业者
数字广告日益依赖视觉内容，但营销人员缺乏严谨方法来理解特定视觉属性对用户参与度的因果影响。本文解决一个核心方法论难题：当处理变量（如模特肤色）嵌入图像内部时，标准双机器学习（DML）方法因视觉编码器将处理信息与混杂变量纠缠而产生严重偏差。为此，我们提出DICE-DML框架，利用生成式AI分离处理与混杂因素。该方法结合三种机制：(1) 深度伪造生成的图像对以分离处理变化；(2) 在成对差值向量上使用DICE-Diff对抗学习，使背景信号抵消，揭示纯处理特征；(3) 几何正交投影移除处理轴分量。模拟实验显示，相比标准DML，DICE-DML将均方根误差降低73%-97%，尤其在零效应点提升达97.5%，有效控制Ⅰ类错误。应用于232,089条Instagram influencer帖子，标准DML结果诊断无效（负的outcome R²），而DICE-DML实现有效混杂控制（R² = 0.63），估计深肤色对点赞数有边缘显著的负向影响（-522次点赞；p = 0.062），远小于标准估计的偏差值。本框架为图像中处理与混杂共存场景下的因果推断提供系统性解决方案。
原文摘要 · Abstract (English)
Digital advertising increasingly relies on visual content, yet marketers lack rigorous methods for understanding how specific visual attributes causally affect consumer engagement. This paper addresses a fundamental methodological challenge: estimating causal effects when the treatment, such as a model's skin tone, is an attribute embedded within the image itself. Standard approaches like Double Machine Learning (DML) fail in this setting because vision encoders entangle treatment information with confounding variables, producing severely biased estimates. We develop DICE-DML (Deepfake-Informed Control Encoder for Double Machine Learning), a framework that leverages generative AI to disentangle treatment from confounders. The approach combines three mechanisms: (1) deepfake-generated image pairs that isolate treatment variation; (2) DICE-Diff adversarial learning on paired difference vectors, where background signals cancel to reveal pure treatment fingerprints; and (3) orthogonal projection that geometrically removes treatment-axis components. In simulations with known ground truth, DICE-DML reduces root mean squared error by 73-97% compared to standard DML, with the strongest improvement (97.5%) at the null effect point, demonstrating robust Type I error control. Applying DICE-DML to 232,089 Instagram influencer posts, we estimate the causal effect of skin tone on engagement. Standard DML produces diagnostically invalid results (negative outcome R^2), while DICE-DML achieves valid confounding control (R^2 = 0.63) and estimates a marginally significant negative effect of darker skin tone (-522 likes; p = 0.062), substantially smaller than the biased standard estimate. Our framework provides a principled approach for causal inference with visual data when treatments and confounders coexist within images.
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