This paper explores how to measure the isolated causal effect of a specific linguistic feature, such as "netspeak" or "profanity," within a text on a reader's perception or behavior, like finding a review helpful. The core challenge lies in accurately representing the non-focal language—everything in the text except the targeted feature—as approximations of this non-focal language can introduce omitted variable bias and impact the accuracy of the estimated effect. The authors introduce a framework and metrics, including fidelity and overlap, to assess the quality of these approximations and the robustness of the resulting effect estimates, demonstrating their method's ability to recover true effects in experiments and analyze how different language representations influence the results.