Learning Adjustment Sets from Observational and Limited Experimental Data

Estimating causal effects from observational data is not always possible due to confounding. Identifying a set of appropriate covariates (adjustment set) and adjusting for their influence can remove confounding bias; however, such a set is typically not identifiable from observational data alone. Experimental data do not have confounding bias, but are typically limited in sample size and can therefore yield imprecise estimates. Furthermore, experimental data often include a limited set of covariates, and therefore provide limited insight into the causal structure of the underlying system. In this work we introduce a method that combines large observational and limited experimental data to identify adjustment sets and improve the estimation of causal effects. The method identifies an adjustment set (if possible) by calculating the marginal likelihood for the experimental data given observationally-derived prior probabilities of potential adjustmen sets. In this way, the method can make inferences that are not possible using only the conditional dependencies and independencies in all the observational and experimental data. We show that the method successfully identifies adjustment sets and improves causal effect estimation in simulated data, and it can sometimes make additional inferences when compared to state-of-the-art methods for combining experimental and observational data.Comment: 10 pages, 5 figure

Achieving Causal Fairness in Recommendation

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed systems utilize user-item interaction data to train models and then generate new data by online recommendation. This feedback loop in recommendation often results in various biases in observational data. The goal of this dissertation is to address challenging issues in achieving causal fairness in recommender systems: achieving user-side fairness and counterfactual fairness in bandit-based recommendation, mitigating confounding and sample selection bias simultaneously in recommendation and robustly improving bandit learning process with biased offline data. In this dissertation, we developed the following algorithms and frameworks for research problems related to causal fairness in recommendation. • We developed a contextual bandit algorithm to achieve group level user-side fairness and two UCB-based causal bandit algorithms to achieve counterfactual individual fairness for personalized recommendation; • We derived sufficient and necessary graphical conditions for identifying and estimating three causal quantities under the presence of confounding and sample selection biases and proposed a framework for leveraging the causal bound derived from the confounded and selection biased offline data to robustly improve online bandit learning process; • We developed a framework for discrimination analysis with the benefit of multiple causes of the outcome variable to deal with hidden confounding; • We proposed a new causal-based fairness notion and developed algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort

Achieving Causal Fairness in Recommendation

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed systems utilize user-item interaction data to train models and then generate new data by online recommendation. This feedback loop in recommendation often results in various biases in observational data. The goal of this dissertation is to address challenging issues in achieving causal fairness in recommender systems: achieving user-side fairness and counterfactual fairness in bandit-based recommendation, mitigating confounding and sample selection bias simultaneously in recommendation and robustly improving bandit learning process with biased offline data. In this dissertation, we developed the following algorithms and frameworks for research problems related to causal fairness in recommendation. • We developed a contextual bandit algorithm to achieve group level user-side fairness and two UCB-based causal bandit algorithms to achieve counterfactual individual fairness for personalized recommendation; • We derived sufficient and necessary graphical conditions for identifying and estimating three causal quantities under the presence of confounding and sample selection biases and proposed a framework for leveraging the causal bound derived from the confounded and selection biased offline data to robustly improve online bandit learning process; • We developed a framework for discrimination analysis with the benefit of multiple causes of the outcome variable to deal with hidden confounding; • We proposed a new causal-based fairness notion and developed algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort

Options for basing Dietary Reference Intakes (DRIs) on chronic disease endpoints: report from a joint US-/Canadian-sponsored working group.

Dietary Reference Intakes (DRIs) are used in Canada and the United States in planning and assessing diets of apparently healthy individuals and population groups. The approaches used to establish DRIs on the basis of classical nutrient deficiencies and/or toxicities have worked well. However, it has proved to be more challenging to base DRI values on chronic disease endpoints; deviations from the traditional framework were often required, and in some cases, DRI values were not established for intakes that affected chronic disease outcomes despite evidence that supported a relation. The increasing proportions of elderly citizens, the growing prevalence of chronic diseases, and the persistently high prevalence of overweight and obesity, which predispose to chronic disease, highlight the importance of understanding the impact of nutrition on chronic disease prevention and control. A multidisciplinary working group sponsored by the Canadian and US government DRI steering committees met from November 2014 to April 2016 to identify options for addressing key scientific challenges encountered in the use of chronic disease endpoints to establish reference values. The working group focused on 3 key questions: 1) What are the important evidentiary challenges for selecting and using chronic disease endpoints in future DRI reviews, 2) what intake-response models can future DRI committees consider when using chronic disease endpoints, and 3) what are the arguments for and against continuing to include chronic disease endpoints in future DRI reviews? This report outlines the range of options identified by the working group for answering these key questions, as well as the strengths and weaknesses of each option