Estimating Position Bias without Intrusive Interventions

Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. While it was recently shown how counterfactual learning-to-rank (LTR) approaches \cite{Joachims/etal/17a} can provably overcome presentation bias when observation propensities are known, it remains to show how to effectively estimate these propensities. In this paper, we propose the first method for producing consistent propensity estimates without manual relevance judgments, disruptive interventions, or restrictive relevance modeling assumptions. First, we show how to harvest a specific type of intervention data from historic feedback logs of multiple different ranking functions, and show that this data is sufficient for consistent propensity estimation in the position-based model. Second, we propose a new extremum estimator that makes effective use of this data. In an empirical evaluation, we find that the new estimator provides superior propensity estimates in two real-world systems -- Arxiv Full-text Search and Google Drive Search. Beyond these two points, we find that the method is robust to a wide range of settings in simulation studies

Controlling Fairness and Bias in Dynamic Learning-to-Rank

Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users, as done by virtually all learning-to-rank algorithms, can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.Comment: First two authors contributed equally. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval 202

One-year follow-up of family versus child CBT for anxiety disorders: Exploring the roles of child age and parental intrusiveness.

ObjectiveTo compare the relative long-term benefit of family-focused cognitive behavioral therapy (FCBT) and child-focused cognitive behavioral therapy (CCBT) for child anxiety disorders at a 1-year follow-up.MethodThirty-five children (6-13 years old) randomly assigned to 12-16 sessions of family-focused CBT (FCBT) or child-focused CBT (CCBT) participated in a 1-year follow-up assessment. Independent evaluators, parents, and children rated anxiety and parental intrusiveness. All were blind to treatment condition and study hypotheses.ResultsChildren assigned to FCBT had lower anxiety scores than children assigned to CCBT on follow-up diagnostician- and parent-report scores, but not child-report scores. Exploratory analyses suggested the advantage of FCBT over CCBT may have been evident more for early adolescents than for younger children and that reductions in parental intrusiveness may have mediated the treatment effect.ConclusionFCBT may yield a stronger treatment effect than CCBT that lasts for at least 1 year, although the lack of consistency across informants necessitates a circumspect view of the findings. The potential moderating and mediating effects considered in this study offer interesting avenues for further study

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Poor infant feeding practices and high prevalence of malnutrition in urban slum child care centres in nairobi: a pilot study

Little is known about the style and quality of feeding and care provided in child day-care centres in slum areas. This study purposively sampled five day-care centres in Nairobi, Kenya, where anthropometric measurements were collected among 33 children aged 6–24 months. Mealtime interactions were further observed in 11 children from four centres, using a standardized data collection sheet. We recorded the child actions, such as mood, interest in food, distraction level, as well as caregiver actions, such as encouragement to eat, level of distraction and presence of neutral actions. Of the 33 children assessed, with a mean age of 15.9 ± 4.9 months, 14 (42%) were female. Undernutrition was found in 13 (39%) children with at least one Z score <−2 or oedema (2): height for age <−2 (11), weight for age <−2 (11), body mass index for age <−2 (4). Rates of undernutrition were highest (9 of 13; 69%) in children aged 18–24 months. Hand-washing before the meal was lacking in all centres. Caregivers were often distracted and rarely encouraged children to feed, with most children eating less than half of their served meal. Poor hygiene coupled with non-responsive care practices observed in the centres is a threat to child health, growth and development

Social Norms as a Cost-Effective Measure of Managing Transport Demand: Evidence from an Experiment on the London Underground

In an effort to cope with increasing passenger demand on its network, Transport for London (TfL) implemented in the second half of 2017 an experiment on one of its busiest metro train platforms. The platform surface was painted to highlight the exact location of the train doors once it comes to a full stop and to direct passengers to wait in parts of the platform that would not obstruct passengers from alighting from the train and leaving the platform. We estimate the effect of this intervention to change passenger behaviour on the platform on train waiting and delay times. We use different sets of assumptions about what the counterfactual change in waiting and delay times would have been in the absence of the intervention. Depending on the assumptions, we find that the intervention has reduced train waiting times between 0 and 6.6%. We also find that this reduction came about mainly through reducing delay times of trains once they are delayed which were cut by between 4.6% and 12.6%. The reductions are not evenly distributed throughout the day, but tend to occur during peak traffic hours. The value of the implied time savings per year are £156,000 at a cost of £25,000, amounting to a return of £6 per £1 investment. If the dwell time reduction could increase train frequency on the affected line by 1 train per hour, however, then TfL could save another £3.6 million

Study Protocol for Investigating Physician Communication Behaviours that Link Physician Implicit Racial Bias and Patient Outcomes in Black Patients with Type 2 Diabetes Using an Exploratory Sequential Mixed Methods Design

Introduction Patient-physician racial discordance is associated with Black patient reports of dissatisfaction and mistrust, which in turn are associated with poor adherence to treatment recommendations and underutilisation of healthcare. Research further has shown that patient dissatisfaction and mistrust are magnified particularly when physicians hold high levels of implicit racial bias. This suggests that physician implicit racial bias manifests in their communication behaviours during medical interactions. The overall goal of this research is to identify physician communication behaviours that link physician implicit racial bias and Black patient immediate (patient-reported satisfaction and trust) and long-term outcomes (eg, medication adherence, self-management and healthcare utilisation) as well as clinical indicators of diabetes control (eg, blood pressure, HbA1c and history of diabetes complication). Methods and analysis Using an exploratory sequential mixed methods research design, we will collect data from approximately 30 family medicine physicians and 300 Black patients with type 2 diabetes mellitus. The data sources will include one physician survey, three patient surveys, medical interaction videos, video elicitation interviews and medical chart reviews. Physician implicit racial bias will be assessed with the physician survey, and patient outcomes will be assessed with the patient surveys and medical chart reviews. In video elicitation interviews, a subset of patients (approximately 20–40) will watch their own interactions while being monitored physiologically to identify evocative physician behaviours. Information from the interview will determine which physician communication behaviours will be coded from medical interactions videos. Coding will be done independently by two trained coders. A series of statistical analyses (zero-order correlations, partial correlations, regressions) will be conducted to identify physician behaviours that are associated significantly with both physician implicit racial bias and patient outcomes