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This talk explores the field of empirical microeconomics, focusing on the importance of causal inference and its application in policy-making. I discuss the concept of evidence in empirical studies, emphasizing the distinction between causality and ...

Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for steering LLMs toward desired attributes often assume unbiased representations ...

Researchers across various fields seek to understand causal relationships but often find controlled experiments impractical. To address this, statistical tools for causal discovery from naturally observed data have become crucial. Non-linear regression ...

Adolescent Idiopathic Scoliosis (AIS) is a prevalent spinal deformity affecting a significant number of adolescents worldwide. Surgical interventions like spinal fusion surgery are common treatments for severe cases, but variability in patient responses ...

This paper compares two estimators—the Border Approach and an Instrumental Variable (IV) estimator—using a unified framework where identifying variation arises from “preference externalities,” following the intuition in Waldfogel (2003). We highlight two ...

Spatiotemporal data, which captures how variables evolve across space and time, is ubiquitous in fields such as environmental science, epidemiology, and urban planning. However, identifying causal relationships in these datasets is challenging due to the ...

Visible-infrared person re-identification (VI-ReID) aims to match individuals across different modalities. Existing methods can learn class-separable features but still struggle with modality gaps within class due to the modality-specific information, ...

With the increasing popularity of online social applications, stickers have become common in online chats. Teaching a model to select the appropriate sticker from a set of candidate stickers based on dialogue context is important for optimizing the user ...

The heterogeneity of medical images poses significant challenges to accurate disease diagnosis. To tackle this issue, the impact of such heterogeneity on the causal relationship between image features and diagnostic labels should be incorporated into ...

This work proposes a novel and simple sequential learning strategy to train models on videos and texts for multimodal sentiment analysis. To estimate sentiment polarities on unseen out-of-distribution data, we introduce a multimodal model that is trained ...

Zero-Shot learning (ZSL) correlates visual samples and shared semantic information to transfer knowledge from seen classes to unseen classes. Existing methods typically establish visual-semantic correlation by aligning visual and semantic features, which ...

Microservice architecture has become a popular architecture adopted by many cloud applications. However, identifying the root cause of a failure in microservice systems is still a challenging and time-consuming task. In recent years, researchers have ...

Although the widespread use of AI systems in today's world is growing, many current AI systems are found vulnerable due to hidden bias and missing information, especially in the most commonly used forecasting system. In this work, we explore the ...

Data from observational studies (OSs) is widely available and readily obtainable yet frequently contains confounding biases. On the other hand, data derived from randomized controlled trials (RCTs) helps to reduce these biases; however, it is expensive ...

Medication recommendation systems are developed to recommend suitable medications tailored to specific patient. Previous researches primarily focus on learning medication representations, which have yielded notable advances. However, these methods are ...

We provide an overview of research on causal inference in the presence of strategic agents. Work in this area uses tools from econometrics, statistics, machine learning, and game theory to infer causal relationships between treatments and outcomes of ...

This paper estimates the drivers of playlist followers on Spotify using a panel data set for 30,000+ popular playlists and combines it with data on how prominently these playlists are featured in the Spotify app.

We provide estimates of the drivers of playlist followers on Spotify. We base our analysis on a unique panel data set for 30,000+ popular playlists and combine it with data on how prominently these playlists are featured in the Spotify app. Using two-way ...

This study evaluates a mathematics mastery learning platform used by over half a million U.S. students in grades 3–8. This platform was examined in prior research via a between-group cross-sectional design on data from a single school year, which ...

Marketing optimization plays an important role to enhance user engagement in online Internet platforms. Existing studies usually formulate this problem as a budget allocation problem and solve it by utilizing two fully decoupled stages, i.e., machine ...

Online controlled experiments play a crucial role in enabling data-driven decisions across a wide range of companies. Variance reduction is an effective technique to improve the sensitivity of experiments, achieving higher statistical power while using ...