AI 中文总结
本文研究AI学习表示作为控制变量时的因果推断有效性，提出交叉拟合DML框架，处理表示误差并实现有效推断，在多模态需求应用中验证了方法的稳健性。
AI 中文摘要
文本、图像和其他丰富的协变量越来越多地被压缩为AI学习的表示，然后用作因果分析中的控制变量。我们研究了这种方法何时有效，并开发了一个用于学习表示因果推断的实用框架。对于一大类估计量，不完美的表示通过两个表示误差的乘积扭曲目标因果参数：一个在结果回归中，一个在平衡权重（或Riesz表示）中。这产生了三个建设性的结果。首先，交叉拟合的双重机器学习（DML）为依赖于表示的靶目标提供了有效的Wald推断。当表示误差较小时，相同的区间覆盖因果参数，甚至可以达到半参数效率界。其次，折内表示学习（或微调）与因果参数的DML推断兼容。为此，我们开发了用于学习和组合表示的凸聚合和星形聚合流程。第三，当表示误差较大时，我们可以提供可解释的敏感性区域及其端点的根号n推断。在一个多模态需求应用中，七个特定表示的估计及其星形聚合都暗示基于排名的价格响应的负近单位弹性，并且结果在报告的敏感性网格上保持稳健。
英文摘要
Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We study when this approach is valid and develop a practical framework for causal inference with learned representations. For a broad class of estimands, an imperfect representation distorts the target causal parameter by the product of two representation errors: one in the outcome regression and one in the balancing weight (or Riesz representer). This yields three constructive results. First, cross-fitted double machine learning (DML) provides valid Wald inference for the representation-dependent target. When representation errors are small, the same interval covers the causal parameter, and it can even attain the semiparametric efficiency bound. Second, fold-wise representation learning (or fine-tuning) is compatible with DML inference for the causal parameter. To this end, we develop convex- and star-aggregation pipelines for learning and combining representations. Third, when representation errors are substantial, we can provide interpretable sensitivity regions and root-$n$ inference for their endpoints. In a multi-modal demand application, seven representation-specific estimates and their star aggregate all imply a negative near-unit elasticity for rank-based price response, and the result remains robust over the reported sensitivity grid.
