Evaluation plays a crucial role in the development of ranking algorithms on search and recommender systems.It enables online platforms to create user-friendly features that drive commercial success in a steady and effective manner.The online environment is particularly conducive to applying causal inference techniques, such as randomized controlled experiments (known as A/B test), which are often more challenging to implement in fields like medicine and public policy.However, businesses face unique challenges when it comes to effective A/B test.Specifically, achieving sufficient statistical power for conversion-based metrics can be time-consuming, especially for significant purchases like booking accommodations.While offline evaluations are quicker and more cost-effective, they often lack accuracy and are inadequate for selecting candidates for A/B test.To address these challenges, we developed interleaving and counterfactual evaluation methods to facilitate rapid online assessments for identifying the most promising candidates for A/B tests.Our approach not only increased the sensitivity of experiments by a factor of up to 100 (depending on the approach and metrics) compared to traditional A/B testing but also streamlined the experimental process.The practical insights gained from usage in production can also benefit organizations with similar interests.

More than a decade ago, CUPED (Controlled Experiments Utilizing Pre-Experiment Data) mainstreamed the idea of variance reduction leveraging pre-experiment covariates. Since its introduction, it has been implemented, extended, and modernized by major online experimentation platforms. Despite the wide adoption, it is known by practitioners that the variance reduction rate from CUPED utilizing pre-experimental data varies case by case and has a theoretical limit. In theory, CUPED can be extended to augment a treatment effect estimator utilizing in-experiment data, but practical guidance on how to construct such an augmentation is lacking. In this article, we fill this gap by proposing a new direction for sensitivity improvement via treatment effect augmentation whereby a target metric of interest is decomposed into components with high signal-to-noise disparity. Inference in the context of this decomposition is developed using both frequentist and Bayesian theory. We provide three real world applications demonstrating different flavors of metric decomposition; these applications illustrate the gain in agility metric decomposition yields relative to an un-decomposed analysis.

Pricing Guidance tools at Airbnb aim to help hosts maximize the earning for each night of stay. For a given listing, the earning-maximization price point of a night can vary greatly with lead-day - the number of days from now until the night of stay. This introduces systematic bias in running marketplace A/B tests to compare the performances of two pricing strategies. Lead-day bias can cause the short-term experiment result to move in the opposite direction to the long-term impact, possibly leading to the suboptimal business decision and customer dissatisfaction. We propose an efficient experimentation approach that corrects for the bias, minimizes the possible negative impact of experimenting, and greatly accelerates the R&D cycle. This paper is the first of its kind to lays out the theoretical framework along with the real-world example that demonstrates the magnitude of the bias. It serves as a conversation starter for such insidious type of experimentation bias that is likely present in other marketplaces of expirable goods such as vacation nights, car rentals, and airline tickets, concert passes, or ride-hailings.

Improving statistical power is a common challenge for online experimentation platforms so that more hypotheses can be tested and lower effect sizes can be detected. To increase the power without increasing the sample size, it is necessary to consider the variance of experimental outcome metrics. Variance reduction was previously applied to online experimentation based on the idea of using pre-experiment covariate data to account for noise in the final metrics. Since this method relies on correlations between pre-experiment covariates and experiment outcomes, its effectiveness can be limited when testing features for specific product surfaces. We were also motivated by the challenge of attributing sparse, delayed binary outcomes to individual user-product interactions. We present two novel methods for variance reduction that rely exclusively on in-experiment data. The first method is a framework for a model-based leading indicator metric which continually estimates progress toward a delayed binary outcome. The second method is a counterfactual treatment exposure index that quantifies the amount that a user is impacted by the treatment. We applied these methods to past experiments and found that both can achieve variance reduction of 50% or more compared to the delayed outcome metric. The substantial reduction in variance afforded by the two methods presented in this paper has enabled Airbnb's experimentation platform to become more agile and innovative.

In online experiments where the intervention is only exposed, or "triggered", for a small subset of the population, it is critical to use variance reduction techniques to estimate treatment effects with sufficient precision to inform business decisions. Trigger-dilute analysis is often used in these situations, and reduce the sampling variance of overall intent-to-treat (ITT) effects by an order of magnitude equal to the inverse of the triggering rate; for example, a triggering rate of 5% corresponds to roughly a 20x reduction in variance. To apply trigger-dilute analysis, one needs to know experimental subjects' triggering counterfactual statuses, i.e., the counterfactual behavior of subjects under both treatment and control conditions. In this paper, we propose an unbiased ITT estimator with reduced variance applicable for experiments where the triggering counterfactual status is only observed in the treatment group. Our method is based on the efficiency augmentation idea of CUPED and draws upon identification frameworks from the principal stratification and instrumental variables literature. The unbiasedness of our estimation approach relies on a testable assumption that the augmentation term used for covariate adjustment equals zero in expectation. Unlike traditional covariate adjustment or principal score modeling approaches, our estimator can incorporate both pre-experiment and in-experiment observations. We demonstrate through both a real-world experiment and simulations that our estimator can remain unbiased and achieve precision improvements as large as if triggering status were fully observed, and in some cases can even outperform trigger-dilute analysis.

A/B tests, or online controlled experiments, are heavily used in industry to evaluate implementations of ideas. While the statistics behind controlled experiments are well documented and some basic pitfalls known, we have observed some seemingly intuitive concepts being touted, including by A/B tool vendors and agencies, which are misleading, often badly so. Our goal is to describe these misunderstandings, the "intuition" behind them, and to explain and bust that intuition with solid statistical reasoning. We provide recommendations that experimentation platform designers can implement to make it harder for experimenters to make these intuitive mistakes.

When interpreting A/B tests, we typically focus only on the statistically significant results and take them by face value. This practice, termed post-selection inference in the statistical literature, may negatively affect both point estimation and uncertainty quantification, and therefore hinder trustworthy decision making in A/B testing. To address this issue, in this paper we explore two seemingly unrelated paths, one based on supervised machine learning and the other on empirical Bayes, and propose post-selection inferential approaches that combine the strengths of both. Through large-scale simulated and empirical examples, we demonstrate that our proposed methodologies stand out among other existing ones in both reducing post-selection biases and improving confidence interval coverage rates, and discuss how they can be conveniently adjusted to real-life scenarios.

During the last decade, the information technology industry has adopted a data-driven culture, relying on online metrics to measure and monitor business performance. Under the setting of big data, the majority of such metrics approximately follow normal distributions, opening up potential opportunities to model them directly without extra model assumptions and solve big data problems via closed-form formulas using distributed algorithms at a fraction of the cost of simulation-based procedures like bootstrap. However, certain attributes of the metrics, such as their corresponding data generating processes and aggregation levels, pose numerous challenges for constructing trustworthy estimation and inference procedures. Motivated by four real-life examples in metric development and analytics for large-scale A/B testing, we provide a practical guide to applying the Delta method, one of the most important tools from the classic statistics literature, to address the aforementioned challenges. We emphasize the central role of the Delta method in metric analytics by highlighting both its classic and novel applications.

The Internet provides developers of connected software, including web sites, applications, and devices, an unprecedented opportunity to accelerate innovation by evaluating ideas quickly and accurately using controlled experiments, also known as A/B tests. From front-end user-interface changes to backend algorithms, from search engines (e.g., Google, Bing, Yahoo!) to retailers (e.g., Amazon, eBay, Etsy) to social networking services (e.g., Facebook, LinkedIn, Twitter) to travel services (e.g., Expedia, Airbnb, Booking.com) to many startups, online controlled experiments are now utilized to make data-driven decisions at a wide range of companies. While the theory of a controlled experiment is simple, and dates back to Sir Ronald A. Fisher's experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, the deployment and evaluation of online controlled experiments at scale (100's of concurrently running experiments) across variety of web sites, mobile apps, and desktop applications presents many pitfalls and new research challenges. In this tutorial we will give an introduction to A/B testing, share key lessons learned from scaling experimentation at Bing to thousands of experiments per year, present real examples, and outline promising directions for future work. The tutorial will go beyond applications of A/B testing in information retrieval and will also discuss on practical and research challenges arising in experimentation on web sites and mobile and desktop apps. Our goal in this tutorial is to teach attendees how to scale experimentation for their teams, products, and companies, leading to better data-driven decisions. We also want to inspire more academic research in the relatively new and rapidly evolving field of online controlled experimentation.

A/B tests (or randomized controlled experiments) play an integral role in the research and development cycles of technology companies. As in classic randomized experiments (e.g., clinical trials), the underlying statistical analysis of A/B tests is based on assuming the randomization unit is independent and identically distributed (\iid). However, the randomization mechanisms utilized in online A/B tests can be quite complex and may render this assumption invalid. Analysis that unjustifiably relies on this assumption can yield untrustworthy results and lead to incorrect conclusions. Motivated by challenging problems arising from actual online experiments, we propose a new method of variance estimation that relies only on practically plausible assumptions, is directly applicable to a wide of range of randomization mechanisms, and can be implemented easily. We examine its performance and illustrate its advantages over two commonly used methods of variance estimation on both simulated and empirical datasets. Our results lead to a deeper understanding of the conditions under which the randomization unit can be treated as \iid In particular, we show that for purposes of variance estimation, the randomization unit can be approximated as \iid when the individual treatment effect variation is small; however, this approximation can lead to variance under-estimation when the individual treatment effect variation is large.

A/B testing is one of the most successful applications of statistical theory in the Internet age. A crucial problem of Null Hypothesis Statistical Testing (NHST), the backbone of A/B testing methodology, is that experimenters are not allowed to continuously monitor the results and make decisions in real time. Many people see this restriction as a setback against the trend in the technology toward real time data analytics. Recently, Bayesian Hypothesis Testing, which intuitively is more suitable for real time decision making, attracted growing interest as a viable alternative to NHST. While corrections of NHST for the continuous monitoring setting are well established in the existing literature and known in A/B testing community, the debate over the issue of whether continuous monitoring is a proper practice in Bayesian testing exists among both academic researchers and general practitioners. In this paper, we formally prove the validity of Bayesian testing under proper stopping rules, and illustrate the theoretical results with concrete simulation illustrations. We point out common bad practices where stopping rules are not proper, and discuss how priors can be learned objectively. General guidelines for researchers and practitioners are also provided.

Online controlled experiments, also called A/B testing, have been established as the mantra for data-driven decision making in many web-facing companies. In recent years, there are emerging research works focusing on building the platform and scaling it up, best practices and lessons learned to obtain trustworthy results, and experiment design techniques and various issues related to statistical inference and testing. However, despite playing a central role in online controlled experiments, there is little published work on treating metric development itself as a data-driven process. In this paper, we focus on the topic of how to develop meaningful and useful metrics for online services in their online experiments, and show how data-driven techniques and criteria can be applied in metric development process. In particular, we emphasize two fundamental qualities for the goal metrics (or Overall Evaluation Criteria) of any online service: directionality and sensitivity. We share lessons on why these two qualities are critical, how to measure these two qualities of metrics of interest, how to develop metrics with clear directionality and high sensitivity by using approaches based on user behavior models and data-driven calibration, and how to choose the right goal metrics for the entire online services.

Online controlled experiments, also called A/B testing, is playing a central role in many data-driven web-facing companies. It is well known and intuitively obvious to many practitioners that when testing a feature with low coverage, analyzing all data collected without zooming into the part that could be affected by the treatment often leads to under-powered hypothesis testing. A common practice is to use triggered analysis. To estimate the overall treatment effect, certain dilution formula is then applied to translate the estimated effect in triggered analysis back to the original all up population. In this paper, we discuss two different types of trigger analyses. We derive correct dilution formulas and show for a set of widely used metrics, namely ratio metrics, correctly deriving and applying those dilution formulas are not trivial. We observe many practitioners in this industry are often applying approximate formulas or even wrong formulas when doing effect dilution calculation. To deal with that, instead of estimating trigger treatment effect followed by effect translation using dilution formula, we aim at combining these two steps into one streamlined analysis, producing more accurate estimation of overall treatment effect together with even higher statistical power than a triggered analysis. The approach we propose in this paper is intuitive, easy to apply and general enough for all types of triggered analyses and all types of metrics.

Web site owners, from small web sites to the largest properties that include Amazon, Facebook, Google, LinkedIn, Microsoft, and Yahoo, attempt to improve their web sites, optimizing for criteria ranging from repeat usage, time on site, to revenue. Having been involved in running thousands of controlled experiments at Amazon, Booking.com, LinkedIn, and multiple Microsoft properties, we share seven rules of thumb for experimenters, which we have generalized from these experiments and their results. These are principles that we believe have broad applicability in web optimization and analytics outside of controlled experiments, yet they are not provably correct, and in some cases exceptions are known.

Online controlled experiments, also called A/B testing, have been established as the mantra for data-driven decision making in many web-facing companies. A/B Testing support decision making by directly comparing two variants at a time. It can be used for comparison between (1) two candidate treatments and (2) a candidate treatment and an established control. In practice, one typically runs an experiment with multiple treatments together with a control to make decision for both purposes simultaneously. This is known to have two issues. First, having multiple treatments increases false positives due to multiple comparison. Second, the selection process causes an upward bias in estimated effect size of the best observed treatment. To overcome these two issues, a two stage process is recommended, in which we select the best treatment from the first screening stage and then run the same experiment with only the selected best treatment and the control in the validation stage. Traditional application of this two-stage design often focus only on results from the second stage. In this paper, we propose a general methodology for combining the first screening stage data together with validation stage data for more sensitive hypothesis testing and more accurate point estimation of the treatment effect. Our method is widely applicable to existing online controlled experimentation systems.

Web-facing companies, including Amazon, eBay, Etsy, Facebook, Google, Groupon, Intuit, LinkedIn, Microsoft, Netflix, Shop Direct, StumbleUpon, Yahoo, and Zynga use online controlled experiments to guide product development and accelerate innovation. At Microsoft's Bing, the use of controlled experiments has grown exponentially over time, with over 200 concurrent experiments now running on any given day. Running experiments at large scale requires addressing multiple challenges in three areas: cultural/organizational, engineering, and trustworthiness. On the cultural and organizational front, the larger organization needs to learn the reasons for running controlled experiments and the tradeoffs between controlled experiments and other methods of evaluating ideas. We discuss why negative experiments, which degrade the user experience short term, should be run, given the learning value and long-term benefits. On the engineering side, we architected a highly scalable system, able to handle data at massive scale: hundreds of concurrent experiments, each containing millions of users. Classical testing and debugging techniques no longer apply when there are billions of live variants of the site, so alerts are used to identify issues rather than relying on heavy up-front testing. On the trustworthiness front, we have a high occurrence of false positives that we address, and we alert experimenters to statistical interactions between experiments. The Bing Experimentation System is credited with having accelerated innovation and increased annual revenues by hundreds of millions of dollars, by allowing us to find and focus on key ideas evaluated through thousands of controlled experiments. A 1% improvement to revenue equals more than $10M annually in the US, yet many ideas impact key metrics by 1% and are not well estimated a-priori. The system has also identified many negative features that we avoided deploying, despite key stakeholders' early excitement, saving us similar large amounts.

Online controlled experiments are at the heart of making data-driven decisions at a diverse set of companies, including Amazon, eBay, Facebook, Google, Microsoft, Yahoo, and Zynga. Small differences in key metrics, on the order of fractions of a percent, may have very significant business implications. At Bing it is not uncommon to see experiments that impact annual revenue by millions of dollars, even tens of millions of dollars, either positively or negatively. With thousands of experiments being run annually, improving the sensitivity of experiments allows for more precise assessment of value, or equivalently running the experiments on smaller populations (supporting more experiments) or for shorter durations (improving the feedback cycle and agility). We propose an approach (CUPED) that utilizes data from the pre-experiment period to reduce metric variability and hence achieve better sensitivity. This technique is applicable to a wide variety of key business metrics, and it is practical and easy to implement. The results on Bing's experimentation system are very successful: we can reduce variance by about 50%, effectively achieving the same statistical power with only half of the users, or half the duration.

Online controlled experiments are often utilized to make data-driven decisions at Amazon, Microsoft, eBay, Facebook, Google, Yahoo, Zynga, and at many other companies. While the theory of a controlled experiment is simple, and dates back to Sir Ronald A. Fisher's experiments at the Rothamsted Agricultural Experimental Station in England in the 1920s, the deployment and mining of online controlled experiments at scale--thousands of experiments now--has taught us many lessons. These exemplify the proverb that the difference between theory and practice is greater in practice than in theory. We present our learnings as they happened: puzzling outcomes of controlled experiments that we analyzed deeply to understand and explain. Each of these took multiple-person weeks to months to properly analyze and get to the often surprising root cause. The root causes behind these puzzling results are not isolated incidents; these issues generalized to multiple experiments. The heightened awareness should help readers increase the trustworthiness of the results coming out of controlled experiments. At Microsoft's Bing, it is not uncommon to see experiments that impact annual revenue by millions of dollars, thus getting trustworthy results is critical and investing in understanding anomalies has tremendous payoff: reversing a single incorrect decision based on the results of an experiment can fund a whole team of analysts. The topics we cover include: the OEC (Overall Evaluation Criterion), click tracking, effect trends, experiment length and power, and carryover effects.