ABSTRACT
For years now, millions of people share details about their real-world experiences on social media, including Twitter, LinkedIn, and Reddit.  Using causal inference methods, we can data mine these social media streams to better understand the common and critical situations people are in, the actions they take, and their outcomes.  Such inferences may be useful for many applications including decision support tools for individuals and analytics to support policy-makers and scientists.  Over the last several years, we have performed several studies across a wide-variety of domains, including health, exercise, education and careers.  In this talk, I describe some of the challenges and promises of extracting and interpreting potential causal relationships in the conversational space of social media, and the growing landscape of research in causal inference over social media datasets.