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In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models. An expressive class of graphical models, relational causal models, can represent and reason about complex dynamic systems exhibiting such cycles or feedback loops. Existing cyclic causal discovery algorithms for learning causal models from observational data assume that the data instances are independent and identically distributed which makes them unsuitable for relational causal models. At the same time, causal discovery algorithms for relational causal models assume acyclicity. In this work, we examine the necessary and sufficient conditions under which a constraint-based relational causal discovery algorithm is sound and complete for cyclic relational causal models. We introduce relational acyclification, an operation specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models. We show that under the assumptions of relational acyclification and sigma-faithfulness, the relational causal discovery algorithm RCD is sound and complete for cyclic relational models. We present experimental results to support our claim. 

Current approaches to A/B testing in networks focus on limiting interference, the concern that treatment effects can “spill over” from treatment nodes to control nodes and lead to biased causal effect estimation. In the presence of interference, two main types of causal effects are direct treatment effects and total treatment effects. In this paper, we propose two network experiment designs that increase the accuracy of direct and total effect estimations in network experiments through minimizing interference between treatment and control units. For direct treatment effect estimation, we present a framework that takes advantage of independent sets and assigns treatment and control only to a set of non-adjacent nodes in a graph, in order to disentangle peer effects from direct treatment effect estimation. For total treatment effect estimation, our framework combines weighted graph clustering and cluster matching approaches to jointly minimize interference and selection bias. Through a series of simulated experiments on synthetic and real-world network datasets, we show that our designs significantly increase the accuracy of direct and total treatment effect estimation in network experiments. 

With the ubiquity of data breaches, forgotten-about files stored in the cloud create latent privacy risks. We take a holistic approach to help users identify sensitive, unwanted files in cloud storage. We first conducted 17 qualitative interviews to characterize factors that make humans perceive a file as sensitive, useful, and worthy of either protection or deletion. Building on our findings, we conducted a primarily quantitative online study. We showed 108 long-term users of Google Drive or Dropbox a selection of files from their accounts. They labeled and explained these files' sensitivity, usefulness, and desired management (whether they wanted to keep, delete, or protect them). For each file, we collected many metadata and content features, building a training dataset of 3,525 labeled files. We then built Aletheia, which predicts a file's perceived sensitivity and usefulness, as well as its desired management. Aletheia improves over state-of-the-art baselines by 26% to 159%, predicting users' desired file-management decisions with 79% accuracy. Notably, predicting subjective perceptions of usefulness and sensitivity led to a 10% absolute accuracy improvement in predicting desired file-management decisions. Aletheia's performance validates a human-centric approach to feature selection when using inference techniques on subjective security-related tasks. It also improves upon the state of the art in minimizing the attack surface of cloud accounts. 

We describe work in progress on detecting and understanding the moral biases of news sources by combining framing theory with natural language processing. First we draw connections between issue-specific frames and moral frames that apply to all issues. Then we analyze the connection between moral frame presence and news source political leaning. We develop and test a simple classification model for detecting the presence of a moral frame, highlighting the need for more sophisticated models. We also discuss some of the annotation and frame detection challenges that can inform future research in this area. 

The causal effect of a treatment can vary from person to per-son based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous treatment effect estimation, has many important applications, from precision medicine to recommender systems. In this paper we define and study a variant of this problem in which an individual-level threshold in treatment needs to be reached, in order to trigger an effect. One of the main contributions of our work is that we do not only estimate heterogeneous treatment effects with fixed treatments but can also prescribe individualized treatments. We propose a tree-based learning method to find the heterogeneity in the treatment effects. Our experimental results on multiple datasets show that our approach can learn the triggers better than existing approaches. 

When users post on social media, they protect their privacy by choosing an access control setting that is rarely revisited. Changes in users' lives and relationships, as well as social media platforms themselves, can cause mismatches between a post's active privacy setting and the desired setting. The importance of managing this setting combined with the high volume of potential friend-post pairs needing evaluation necessitate a semi-automated approach. We attack this problem through a combination of a user study and the development of automated inference of potentially mismatched privacy settings. A total of 78 Facebook users reevaluated the privacy settings for five of their Facebook posts, also indicating whether a selection of friends should be able to access each post. They also explained their decision. With this user data, we designed a classifier to identify posts with currently incorrect sharing settings. This classifier shows a 317% improvement over a baseline classifier based on friend interaction. We also find that many of the most useful features can be collected without user intervention, and we identify directions for improving the classifier's accuracy. 

Online archives, including social media and cloud storage, store vast troves of personal data accumulated over many years. Recent work suggests that users feel the need to retrospectively manage security and privacy for this huge volume of content. However, few mechanisms and systems help these users complete this daunting task. To that end, we propose the creation of usable retrospective data management mechanisms, outlining our vision for a possible architecture to address this challenge.