  Statistical methods applied to social media posts shed light on the dynamics
of online dialogue. For example, users' wording choices predict their
persuasiveness and users adopt the language patterns of other dialogue
participants. In this paper, we estimate the causal effect of reply tones in
debates on linguistic and sentiment changes in subsequent responses. The
challenge for this estimation is that a reply's tone and subsequent responses
are confounded by the users' ideologies on the debate topic and their emotions.
To overcome this challenge, we learn representations of ideology using
generative models of text. We study debates from 4Forums and compare annotated
tones of replying such as emotional versus factual, or reasonable versus
attacking. We show that our latent confounder representation reduces bias in
ATE estimation. Our results suggest that factual and asserting tones affect
dialogue and provide a methodology for estimating causal effects from text.
