We address causal inference with text documents. For example, does adding a theorem to a paper affect its chance of acceptance? Does reporting the gender of a forum post author affect the popularity of the post? We estimate these effects from observational data, where they may be confounded by features of the text such as the subject or writing quality. Although the text suffices for causal adjustment, it is prohibitively high-dimensional. The challenge is to find a low-dimensional text representation that can be used in causal inference. A key insight is that causal adjustment requires only the aspects of text that are predictive of both the treatment and outcome. Our proposed method adapts deep language models to learn low-dimensional embeddings from text that predict these values well; these embeddings suffice for causal adjustment. We establish theoretical properties of this method. We study it empirically on semi-simulated and real data on paper acceptance and forum post popularity. Code is available at https://github.com/blei-lab/causal-text-embeddings.

该论文提出了一种基于因果效应的文本嵌入学习方法，通过有监督降维和有效的文本特征筛选建立起高效的低维表示，使得足够保留有关因果关系的信息，通过实验验证了该方法相比其它文本嵌入方法更能提高因果效应评估的准确性和效率，并通过具体案例分析论证了该方法的实际应用价值。

为因果推断调整文本嵌入