New User Event Prediction Through the Lens of Causal Inference

Modeling and analysis for event series generated by heterogeneous users of various behavioral patterns are closely involved in our daily lives, including credit card fraud detection, online platform user recommendation, and social network analysis. The most commonly adopted approach to this task is to classify users into behavior-based categories and analyze each of them separately. However, this approach requires extensive data to fully understand user behavior, presenting challenges in modeling newcomers without historical knowledge. In this paper, we propose a novel discrete event prediction framework for new users through the lens of causal inference. Our method offers an unbiased prediction for new users without needing to know their categories. We treat the user event history as the ''treatment'' for future events and the user category as the key confounder. Thus, the prediction problem can be framed as counterfactual outcome estimation, with the new user model trained on an adjusted dataset where each event is re-weighted by its inverse propensity score. We demonstrate the superior performance of the proposed framework with a numerical simulation study and two real-world applications, including Netflix rating prediction and seller contact prediction for customer support at Amazon.

TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizing

In this paper, we leverage the power of latent diffusion models to generate synthetic time series tabular data. Along with the temporal and feature correlations, the heterogeneous nature of the feature in the table has been one of the main obstacles in time series tabular data modeling. We tackle this problem by combining the ideas of the variational auto-encoder (VAE) and the denoising diffusion probabilistic model (DDPM). Our model named as \texttt{TimeAutoDiff} has several key advantages including (1) Generality: the ability to handle the broad spectrum of time series tabular data from single to multi-sequence datasets; (2) Good fidelity and utility guarantees: numerical experiments on six publicly available datasets demonstrating significant improvements over state-of-the-art models in generating time series tabular data, across four metrics measuring fidelity and utility; (3) Fast sampling speed: entire time series data generation as opposed to the sequential data sampling schemes implemented in the existing diffusion-based models, eventually leading to significant improvements in sampling speed, (4) Entity conditional generation: the first implementation of conditional generation of multi-sequence time series tabular data with heterogenous features in the literature, enabling scenario exploration across multiple scientific and engineering domains. Codes are in preparation for release to the public, but available upon request.

Counterfactual Fairness through Transforming Data Orthogonal to Bias

Machine learning models have shown exceptional prowess in solving complex issues across various domains. Nonetheless, these models can sometimes exhibit biased decision-making, leading to disparities in treatment across different groups. Despite the extensive research on fairness, the nuanced effects of multivariate and continuous sensitive variables on decision-making outcomes remain insufficiently studied. We introduce a novel data pre-processing algorithm, Orthogonal to Bias (OB), designed to remove the influence of a group of continuous sensitive variables, thereby facilitating counterfactual fairness in machine learning applications. Our approach is grounded in the assumption of a jointly normal distribution within a structural causal model (SCM), proving that counterfactual fairness can be achieved by ensuring the data is uncorrelated with sensitive variables. The OB algorithm is model-agnostic, catering to a wide array of machine learning models and tasks, and includes a sparse variant to enhance numerical stability through regularization. Through empirical evaluation on simulated and real-world datasets - including the adult income and the COMPAS recidivism datasets - our methodology demonstrates its capacity to enable fairer outcomes without compromising accuracy.

Bayesian Optimization with Hidden Constraints via Latent Decision Models

Bayesian optimization (BO) has emerged as a potent tool for addressing intricate decision-making challenges, especially in public policy domains such as police districting. However, its broader application in public policymaking is hindered by the complexity of defining feasible regions and the high-dimensionality of decisions. This paper introduces the Hidden-Constrained Latent Space Bayesian Optimization (HC-LSBO), a novel BO method integrated with a latent decision model. This approach leverages a variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision space and a lower-dimensional latent space. By doing so, HC-LSBO captures the nuances of hidden constraints inherent in public policymaking, allowing for optimization in the latent space while evaluating objectives in the original space. We validate our method through numerical experiments on both synthetic and real data sets, with a specific focus on large-scale police districting problems in Atlanta, Georgia. Our results reveal that HC-LSBO offers notable improvements in performance and efficiency compared to the baselines.

Detecting Electricity Service Equity Issues with Transfer Counterfactual Learning on Large-Scale Outage Datasets

Energy justice is a growing area of interest in interdisciplinary energy research. However, identifying systematic biases in the energy sector remains challenging due to confounding variables, intricate heterogeneity in treatment effects, and limited data availability. To address these challenges, we introduce a novel approach for counterfactual causal analysis centered on energy justice. We use subgroup analysis to manage diverse factors and leverage the idea of transfer learning to mitigate data scarcity in each subgroup. In our numerical analysis, we apply our method to a large-scale customer-level power outage data set and investigate the counterfactual effect of demographic factors, such as income and age of the population, on power outage durations. Our results indicate that low-income and elderly-populated areas consistently experience longer power outages, regardless of weather conditions. This points to existing biases in the power system and highlights the need for focused improvements in areas with economic challenges.

Uncertainty-Aware Robust Learning on Noisy Graphs

Graph neural networks have shown impressive capabilities in solving various graph learning tasks, particularly excelling in node classification. However, their effectiveness can be hindered by the challenges arising from the widespread existence of noisy measurements associated with the topological or nodal information present in real-world graphs. These inaccuracies in observations can corrupt the crucial patterns within the graph data, ultimately resulting in undesirable performance in practical applications. To address these issues, this paper proposes a novel uncertainty-aware graph learning framework motivated by distributionally robust optimization. Specifically, we use a graph neural network-based encoder to embed the node features and find the optimal node embeddings by minimizing the worst-case risk through a minimax formulation. Such an uncertainty-aware learning process leads to improved node representations and a more robust graph predictive model that effectively mitigates the impact of uncertainty arising from data noise. Our experimental result shows that the proposed framework achieves superior predictive performance compared to the state-of-the-art baselines under various noisy settings.

Counterfactual Generative Models for Time-Varying Treatments

Estimating average causal effects is a common practice to test new treatments. However, the average effect ''masks'' important individual characteristics in the counterfactual distribution, which may lead to safety, fairness, and ethical concerns. This issue is exacerbated in the temporal setting, where the treatment is sequential and time-varying, leading to an intricate influence on the counterfactual distribution. In this paper, we propose a novel conditional generative modeling approach to capture the whole counterfactual distribution, allowing efficient inference on certain statistics of the counterfactual distribution. This makes the proposed approach particularly suitable for healthcare and public policy making. Our generative modeling approach carefully tackles the distribution mismatch in the observed data and the targeted counterfactual distribution via a marginal structural model. Our method outperforms state-of-the-art baselines on both synthetic and real data.

Conditional Generative Modeling is All You Need for Marked Temporal Point Processes

Recent advancements in generative modeling have made it possible to generate high-quality content from context information, but a key question remains: how to teach models to know when to generate content? To answer this question, this study proposes a novel event generative model that draws its statistical intuition from marked temporal point processes, and offers a clean, flexible, and computationally efficient solution for a wide range of applications involving multi-dimensional marks. We aim to capture the distribution of the point process without explicitly specifying the conditional intensity or probability density. Instead, we use a conditional generator that takes the history of events as input and generates the high-quality subsequent event that is likely to occur given the prior observations. The proposed framework offers a host of benefits, including exceptional efficiency in learning the model and generating samples, as well as considerable representational power to capture intricate dynamics in multi- or even high-dimensional event space. Our numerical results demonstrate superior performance compared to other state-of-the-art baselines.

Neural Spectral Marked Point Processes

Self- and mutually-exciting point processes are popular models in machine learning and statistics for dependent discrete event data. To date, most existing models assume stationary kernels (including the classical Hawkes processes) and simple parametric models. Modern applications with complex event data require more general point process models that can incorporate contextual information of the events, called marks, besides the temporal and location information. Moreover, such applications often require non-stationary models to capture more complex spatio-temporal dependence. To tackle these challenges, a key question is to devise a versatile influence kernel in the point process model. In this paper, we introduce a novel and general neural network-based non-stationary influence kernel with high expressiveness for handling complex discrete events data while providing theoretical performance guarantees. We demonstrate the superior performance of our proposed method compared with the state-of-the-art on synthetic and real data.

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data

Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease. Understanding the spatio-temporal dynamics of hotspot events is of great importance to support policy decisions and prevent large-scale outbreaks. This paper presents a spatio-temporal Bayesian framework for early detection of COVID-19 hotspots (at the county level) in the United States. We assume both the observed number of cases and hotspots depend on a class of latent random variables, which encode the underlying spatio-temporal dynamics of the transmission of COVID-19. Such latent variables follow a zero-mean Gaussian process, whose covariance is specified by a non-stationary kernel function. The most salient feature of our kernel function is that deep neural networks are introduced to enhance the model's representative power while still enjoying the interpretability of the kernel. We derive a sparse model and fit the model using a variational learning strategy to circumvent the computational intractability for large data sets. Our model demonstrates better interpretability and superior hotspot-detection performance compared to other baseline methods.