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Found 20 papers, 9 papers with code
Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization
We then use a meta-learning framework to examine heterogeneous treatment effects of intergroup interactions on an individual's group conformity in the light of communal, political, and socio-economic events.
Leveraging heterogeneous spillover effects in maximizing contextual bandit rewards
However, current approaches ignore potential spillover between interacting users, where the action of one user can impact the actions and rewards of other users.
Mitigating Cold-start Forecasting using Cold Causal Demand Forecasting Model
Our experiments demonstrate that the CDF-cold framework outperforms state-of-the-art forecasting models in predicting future values of multivariate time series data.
Contagion Effect Estimation Using Proximal Embeddings
Contagion effect refers to the causal effect of peers' behavior on the outcome of an individual in social networks.
Inferring Causal Effects Under Heterogeneous Peer Influence
Heterogeneous peer influence (HPI) occurs when a unit's outcome is influenced differently by different peers based on their attributes and relationships, or when each unit has a different susceptibility to peer influence.
Data-Driven Estimation of Heterogeneous Treatment Effects
In this work, we provide a survey of state-of-the-art data-driven methods for heterogeneous treatment effect estimation using machine learning, broadly categorizing them as methods that focus on counterfactual prediction and methods that directly estimate the causal effect.
Learning Relational Causal Models with Cycles through Relational Acyclification
We introduce relational acyclification, an operation specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models.
Non-Parametric Inference of Relational Dependence
Independence testing plays a central role in statistical and causal inference from observational data.
Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty
To address this problem, we develop a feature selection method that considers each feature's value for HTE estimation and learns the relevant parts of the causal structure from data.
Relational Causal Models with Cycles:Representation and Reasoning
To facilitate cycles in relational representation and learning, we introduce relational $\sigma$-separation, a new criterion for understanding relational systems with feedback loops.
Heterogeneous Peer Effects in the Linear Threshold Model
The Linear Threshold Model is a widely used model that describes how information diffuses through a social network.
RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems
Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data.
Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game
The popularity of online gaming has grown dramatically, driven in part by streaming and the billion-dollar e-sports industry.
Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions
Cannabis legalization has been welcomed by many U. S. states but its role in escalation from tobacco e-cigarette use to cannabis vaping is unclear.
Detecting and understanding moral biases in news
We describe work in progress on detecting and understanding the moral biases of news sources by combining framing theory with natural language processing.
Minimizing Interference and Selection Bias in Network Experiment Design
Here, we show that cluster randomization does not ensure sufficient node randomization and it can lead to selection bias in which treatment and control nodes represent different populations of users.
Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis
We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets.
Correcting for Selection Bias in Learning-to-rank Systems
Click data collected by modern recommendation systems are an important source of observational data that can be utilized to train learning-to-rank (LTR) systems.
Variable-lag Granger Causality for Time Series Analysis
Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay.
Learning Triggers for Heterogeneous Treatment Effects
The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions.
