Answering What If, Should I and Other Expectation Exploration Queries Using Causal Inference over Longitudinal Data

Many people use web search engines for expectation exploration: exploring what might happen if they take some action, or how they should expect some situation to evolve. While search engines have databases to provide structured answers to many questions, there is no database about the outcomes of actions or the evolution of situations. The information we need to answer such questions, however, is already being recorded. On social media, for example, hundreds of millions of people are publicly reporting about the actions they take and the situations they are in, and an increasing range of events and activities experienced in their lives over time.
Here, we show how causal inference methods can be applied to such data to generate answers for expectation exploration queries. This paper describes a system implementation for running ad-hoc online causal inference analyses. The analysis results can be used to generate pros/cons lists for decision support, timeline representations to show how situations evolve, and be embedded in many other decision support and planning applications. We discuss potential methods for evaluating the fundamental quality of inference results and judge the short-term and long-term usefulness of information for users.