Why did Alice develop heart disease in her fifties? What led to the volatility in the U.S. stock market in August 2011? Who shot John F. Kennedy? The inference method described so far aims to find causal relationships that hold in general, while these questions seek causal explanations for one-time events. We do not want to know what causes heart disease, stock market crashes, or death by shooting in general but rather aim to determine why each of these particular events happened. This is a challenging problem, as we need to make such determinations with incomplete and often conflicting information. Few algorithmic methods have been developed to automate this process, yet this may have wide applications to situations with continuous monitoring, such as in intensive care units. Physicians there are overwhelmed with information and need to distinguish between factors causing a particular patient's current symptoms and side effects of their underlying illness to determine the best course of treatment.

This chapter begins in section 6.1 with a discussion of the distinction between type and token causality, and review of methods for token-level reasoning. In section 6.2, I introduce a new approach that links the type-level theory developed in earlier chapters with token-level observation sequences and develops methods for ranking explanations with incomplete and uncertain information. Finally, this is illustrated through worked out examples in section 6.3 and analysis of test cases that have proven difficult for prior approaches in section 6.4.