arXiv subjects

Ye Tu

Publications and source records attributed to Ye Tu.

5 recordsLinked to original sources

arXiv2022

Generalized Causal Tree for Uplift Modeling

Uplift modeling is crucial in various applications ranging from marketing and policy-making to personalized recommendations. The main objective is to learn optimal treatment allocations for a heterogeneous population. A primary line of existing work modifies the loss function of the decision tree algorithm to identify cohorts with heterogeneous treatment effects. Another line of work estimates the individual treatment effects separately for the treatment group and the control group using off-the-shelf supervised learning algorithms. The former approach that directly models the heterogeneous treatment effect is known to outperform the latter in practice. However, the existing tree-based methods are mostly limited to a single treatment and a single control use case, except for a handful of extensions to multiple discrete treatments. In this paper, we propose a generalization of tree-based approaches to tackle multiple discrete and continuous-valued treatments. We focus on a generalization of the well-known causal tree algorithm due to its desirable statistical properties, but our generalization technique can be applied to other tree-based approaches as well. The efficacy of our proposed method is demonstrated using experiments and real data examples.

stat.ME↗

arXiv2021

Feedback Shaping: A Modeling Approach to Nurture Content Creation

Social media platforms bring together content creators and content consumers through recommender systems like newsfeed. The focus of such recommender systems has thus far been primarily on modeling the content consumer preferences and optimizing for their experience. However, it is equally critical to nurture content creation by prioritizing the creators' interests, as quality content forms the seed for sustainable engagement and conversations, bringing in new consumers while retaining existing ones. In this work, we propose a modeling approach to predict how feedback from content consumers incentivizes creators. We then leverage this model to optimize the newsfeed experience for content creators by reshaping the feedback distribution, leading to a more active content ecosystem. Practically, we discuss how we balance the user experience for both consumers and creators, and how we carry out online A/B tests with strong network effects. We present a deployed use case on the LinkedIn newsfeed, where we used this approach to improve content creation significantly without compromising the consumers' experience.

cs.CY↗

arXiv2019

Causal inference from observational data: Estimating the effect of contributions on visitation frequency atLinkedIn

Randomized experiments (A/B testings) have become the standard way for web-facing companies to guide innovation, evaluate new products, and prioritize ideas. There are times, however, when running an experiment is too complicated (e.g., we have not built the infrastructure), costly (e.g., the intervention will have a substantial negative impact on revenue), and time-consuming (e.g., the effect may take months to materialize). Even if we can run an experiment, knowing the magnitude of the impact will significantly accelerate the product development life cycle by helping us prioritize tests and determine the appropriate traffic allocation for different treatment groups. In this setting, we should leverage observational data to quickly and cost-efficiently obtain a reliable estimate of the causal effect. Although causal inference from observational data has a long history, its adoption by data scientist in technology companies has been slow. In this paper, we rectify this by providing a brief introduction to the vast field of causal inference with a specific focus on the tools and techniques that data scientist can directly leverage. We illustrate how to apply some of these methodologies to measure the effect of contributions (e.g., post, comment, like or send private messages) on engagement metrics. Evaluating the impact of contributions on engagement through an A/B test requires encouragement design and the development of non-standard experimentation infrastructure, which can consume a tremendous amount of time and financial resources. We present multiple efficient strategies that exploit historical data to accurately estimate the contemporaneous (or instantaneous) causal effect of a user's contribution on her own and her neighbors' (i.e., the users she is connected to) subsequent visitation frequency. We apply these tools to LinkedIn data for several million members.

stat.AP↗

arXiv2019

A/B Testing in Dense Large-Scale Networks: Design and Inference

Design of experiments and estimation of treatment effects in large-scale networks, in the presence of strong interference, is a challenging and important problem. Most existing methods' performance deteriorates as the density of the network increases. In this paper, we present a novel strategy for accurately estimating the causal effects of a class of treatments in a dense large-scale network. First, we design an approximate randomized controlled experiment by solving an optimization problem to allocate treatments in the presence of competition among neighboring nodes. Then we apply an importance sampling adjustment to correct for any leftover bias (from the approximation) in estimating average treatment effects. We provide theoretical guarantees, verify robustness in a simulation study, and validate the scalability and usefulness of our procedure in a real-world experiment on a large social network.

stat.ME↗

arXiv2019

Personalized Treatment Selection using Causal Heterogeneity

Randomized experimentation (also known as A/B testing or bucket testing) is widely used in the internet industry to measure the metric impact obtained by different treatment variants. A/B tests identify the treatment variant showing the best performance, which then becomes the chosen or selected treatment for the entire population. However, the effect of a given treatment can differ across experimental units and a personalized approach for treatment selection can greatly improve upon the usual global selection strategy. In this work, we develop a framework for personalization through (i) estimation of heterogeneous treatment effect at either a cohort or member-level, followed by (ii) selection of optimal treatment variants for cohorts (or members) obtained through (deterministic or stochastic) constrained optimization. We perform a two-fold evaluation of our proposed methods. First, a simulation analysis is conducted to study the effect of personalized treatment selection under carefully controlled settings. This simulation illustrates the differences between the proposed methods and the suitability of each with increasing uncertainty. We also demonstrate the effectiveness of the method through a real-life example related to serving notifications at Linkedin. The solution significantly outperformed both heuristic solutions and the global treatment selection baseline leading to a sizable win on top-line metrics like member visits.

stat.ME↗