Understanding causal relationships among the variables of a system is paramount to explain and control its behavior. For many real-world systems, however, the true causal graph is not readily available and one must resort to predictions made by algorithms or domain experts. Therefore, metrics that quantitatively assess the goodness of a causal graph provide helpful checks before using it in downstream tasks. Existing metrics provide an absolute number of inconsistencies between the graph and the observed data, and without a baseline, practitioners are left to answer the hard question of how many such inconsistencies are acceptable or expected. Here, we propose a novel consistency metric by constructing a baseline through node permutations. By comparing the number of inconsistencies with those on the baseline, we derive an interpretable metric that captures whether the graph is significantly better than random. Evaluating on both simulated and real data sets from various domains, including biology and cloud monitoring, we demonstrate that the true graph is not falsified by our metric, whereas the wrong graphs given by a hypothetical user are likely to be falsified.

Statistical prediction models are often trained on data that is drawn from different probability distributions than their eventual use cases. One approach to proactively prepare for these shifts harnesses the intuition that causal mechanisms should remain invariant between environments. Here we focus on a challenging setting in which the causal and anticausal variables of the target are unobserved. Leaning on information theory, we develop feature selection and engineering techniques for the observed downstream variables that act as proxies. We identify proxies that help to build stable models and moreover utilize auxiliary training tasks to extract stability-enhancing information from proxies. We demonstrate the effectiveness of our techniques on synthetic and real data.

Despite the increasing relevance of forecasting methods, causal implications of these algorithms remain largely unexplored. This is concerning considering that, even under simplifying assumptions such as causal sufficiency, the statistical risk of a model can differ significantly from its causal risk. Here, we study the problem of causal generalization—generalizing from the observational to interventional distributions—in forecasting. Our goal is to find answers to the question: How does the efficacy of an autoregressive (VAR) model in predicting statistical associations compare with its ability to predict under interventions? To this end, we introduce the framework of causal learning theory for forecasting. Using this framework, we obtain a characterization of the difference between statistical and causal risks, which helps identify sources of divergence between them. Under causal sufficiency, the problem of causal generalization amounts to learning under covariate shifts albeit with additional structure (restriction to interventional distributions under the VAR model). This structure allows us to obtain uniform convergence bounds on causal generalizability for the class of VAR models. To the best of our knowledge, this is the first work that provides theoretical guarantees for causal generalization in the time-series setting.

Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the dataset, and the specific model. We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions. It is deeply integrated into Amazon SageMaker, a fully managed service that enables data scientists and developers to build, train, and deploy ML models at any scale. Clarify supports bias detection and feature importance computation across the ML lifecycle, during data preparation, model evaluation, and post-deployment monitoring. We outline the desiderata derived from customer input, the modular architecture, and the methodology for bias and explanation computations. Further, we describe the technical challenges encountered and the tradeoffs we had to make. For illustration, we discuss two customer use cases. We present our deployment results including qualitative customer feedback and a quantitative evaluation. Finally, we summarize lessons learned, and discuss best practices for the successful adoption of fairness and explanation tools in practice.

Linear dynamical systems are a fundamental and powerful parametric model class. However, identifying the parameters of a linear dynamical system is a venerable task, permitting provably efficient solutions only in special cases. This work shows that the eigenspectrum of unknown linear dynamics can be identified without full system identification. We analyze a computationally efficient and provably convergent algorithm to estimate the eigenvalues of the state-transition matrix in a linear dynamical system.When applied to time series clustering, our algorithm can efficiently cluster multi-dimensional time series with temporal offsets and varying lengths, under the assumption that the time series are generated from linear dynamical systems. Evaluating our algorithm on both synthetic data and real electrocardiogram (ECG) signals, we see improvements in clustering quality over existing baselines.

Today, websites commonly use third party web analytics services t obtain aggregate information about users that visit their sites. This information includes demographics and visits to other sites as well as user behavior within their own sites. Unfortunately, to obtain this aggregate information, web analytics services track individual user browsing behavior across the web. This violation of user privacy has been strongly criticized, resulting in tools that block such tracking as well as anti-tracking legislation and standards such as Do-Not-Track. These efforts, while improving user privacy, degrade the quality of web analytics. This paper presents the first design of a system that provides web analytics without tracking. The system gives users differential privacy guarantees, can provide better quality analytics than current services, requires no new organizational players, and is practical to deploy. This paper describes and analyzes the design, gives performance benchmarks, and presents our implementation and deployment across several hundred users.

Mobile advertising is an increasingly important driver in the Internet economy. We point out fundamental trade-offs between important variables in the mobile advertisement ecosystem. In order to increase relevance, ad campaigns tend to become more targeted and personalized by using context information extracted from user's interactions and smartphone's sensors. This raises privacy concerns that are hard to overcome due to the limited resources (energy and bandwidth) available on the phones. We point out that in the absence of a trusted third party, it is impossible to maximize these three variables - ad relevance, privacy, and efficiency - in a single system. This leads to the natural question: can we formalize a common framework for personalized ad delivery that can be instantiated to any desired trade-off point? We propose such a flexible ad-delivery framework where personalization is done jointly by the server and the phone. We show that the underlying optimization problem is NP-hard and present an efficient algorithm with a tight approximation guarantee.