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Tag: effects

  • Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning

    Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning arXiv:2511.05050v1 Announce Type: new Abstract: In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedasticity. Traditional causal inference often focuses on unidirectional effects, overlooking the common bidirectional relationships in real-world phenomena.…

  • Using latent representations to link disjoint longitudinal data for mixed-effects regression

    Using latent representations to link disjoint longitudinal data for mixed-effects regression arXiv:2510.25531v1 Announce Type: new Abstract: Many rare diseases offer limited established treatment options, leading patients to switch therapies when new medications emerge. To analyze the impact of such treatment switches within the low sample size limitations of rare disease trials, it is important to…

  • Kernel Treatment Effects with Adaptively Collected Data

    Kernel Treatment Effects with Adaptively Collected Data arXiv:2510.10245v1 Announce Type: new Abstract: Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d. assumptions that underpins classical asymptotics. At the same time, many questions of interest are distributional, extending beyond average effects. Kernel treatment effects (KTE) provide a…

  • Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks

    Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks arXiv:2509.22760v1 Announce Type: new Abstract: We develop a physics-informed neural network (PINN) framework for parameter estimation in fractional-order SEIRD epidemic models. By embedding the Caputo fractional derivative into the network residuals via the L1 discretization scheme, our method simultaneously reconstructs epidemic…

  • Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery

    Causal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery arXiv:2509.05775v1 Announce Type: new Abstract: Estimating heterogeneous treatment effects is critical in domains such as personalized medicine, resource allocation, and policy evaluation. A central challenge lies in identifying subpopulations that respond differently to interventions, thereby enabling more targeted and effective decision-making. While clustering methods…

  • Estimating Treatment Effects with Independent Component Analysis

    Estimating Treatment Effects with Independent Component Analysis arXiv:2507.16467v1 Announce Type: new Abstract: The field of causal inference has developed a variety of methods to accurately estimate treatment effects in the presence of nuisance. Meanwhile, the field of identifiability theory has developed methods like Independent Component Analysis (ICA) to identify latent sources and mixing weights from…

  • The Hidden Trap of Fixed and Random Effects

    The Hidden Trap of Fixed and Random Effects My lesson of how blindly over-controlling for noise can erase the effects you are measuring The post The Hidden Trap of Fixed and Random Effects appeared first on Towards Data Science. Ngoc Doan Go to original source

  • Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data

    Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data arXiv:2507.03681v1 Announce Type: new Abstract: Randomized trials are typically designed to detect average treatment effects but often lack the statistical power to uncover effect heterogeneity over patient characteristics, limiting their value for personalized decision-making. To address this, we propose the QR-learner, a model-agnostic…

  • Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models

    Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models arXiv:2506.04945v1 Announce Type: new Abstract: Estimating causal effects of joint interventions on multiple variables is crucial in many domains, but obtaining data from such simultaneous interventions can be challenging. Our study explores how to learn joint interventional effects using only observational data and single-variable interventions.…

  • Interpretability of Graph Neural Networks to Assert Effects of Global Change Drivers on Ecological Networks

    Interpretability of Graph Neural Networks to Assert Effects of Global Change Drivers on Ecological Networks arXiv:2503.15107v1 Announce Type: new Abstract: Pollinators play a crucial role for plant reproduction, either in natural ecosystem or in human-modified landscape. Global change drivers,including climate change or land use modifications, can alter the plant-pollinator interactions. To assert the potential influence…