In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: This paper concerns the probabilistic evalu­ ation of the effects of actions in the presence of unmeasured variables. We show that the identification of causal effect between a sin­ gleton variable X and a set of variables Y can be accomplished systematically, in time polynomial in the number of variables in the graph. When the causal effect is identifiable, a closed-form expression can be obtained for the probability that the action will achieve a specified goal, or a set of goals.

A: Testing Identifiability of Causal Effects
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Q: Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential selfharm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in

A: Triaging Content Severity in Online Mental Health Forums∗
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Q: Languages for open-universe probabilistic models (OUPMs) can represent situations with an unknown number of objects and identity uncertainty. While such cases arise in a wide range of important real-world applications, existing general purpose inference methods for OUPMs are far less efficient than those available for more restricted languages and model classes. This paper goes some way to remedying this deficit by introducing, and proving correct, a generalization of Gibbs sampling to partial worlds with possibly varying model structure. Our approach draws on and extends previous generic OUPM inference methods, as well as auxiliary variable samplers for nonparametric mixture models. It has been implemented for BLOG, a well-known OUPM language. Combined with compile-time optimizations, the resulting algorithm yields very substantial speedups over existing methods on several test cases, and substantially improves the practicality of OUPM languages generally.

A:
Gibbs Sampling in Open-Universe Stochastic Languages
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