  Understanding what leads to effective conversations can aid the design of
better computer-mediated communication platforms. In particular, prior
observational work has sought to identify behaviors of individuals that
correlate to their conversational efficiency. However, translating such
correlations to causal interpretations is a necessary step in using them in a
prescriptive fashion to guide better designs and policies.
  In this work, we formally describe the problem of drawing causal links
between conversational behaviors and outcomes. We focus on the task of
determining a particular type of policy for a text-based crisis counseling
platform: how best to allocate counselors based on their behavioral tendencies
exhibited in their past conversations. We apply arguments derived from causal
inference to underline key challenges that arise in conversational settings
where randomized trials are hard to implement. Finally, we show how to
circumvent these inference challenges in our particular domain, and illustrate
the potential benefits of an allocation policy informed by the resulting
prescriptive information.
