Papers Heterogeneous Treatment Effect Estimation
“Heterogeneous Treatment Effect Estimation” 태그가 달린 논문 44편 · 필터 해제
Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation
Estimating heterogeneous treatment effects is central to targeted interventions, such as personalized promotions and precision medicine. We focus on the conditional average treatment effect (CATE), a standard estimand fo…
Heterogeneous Treatment Effect EstimationRepresentation LearningA Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
Estimating Conditional Average Treatment Effects (CATE) at the individual level is central to precision marketing, yet systematic benchmarking of uplift modeling methods at industrial scale remains limited. We present Up…
Heterogeneous Treatment Effect EstimationSurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
Estimating heterogeneous treatment effects (HTEs) from right-censored survival data is critical in high-stakes applications such as precision medicine and individualized policy-making. Yet, the survival analysis setting …
Heterogeneous Treatment Effect EstimationGuardrailed Uplift Targeting: A Causal Optimization Playbook for Marketing Strategy
This paper introduces a marketing decision framework that optimizes customer targeting by integrating heterogeneous treatment effect estimation with explicit business guardrails. The objective is to maximize revenue and …
Heterogeneous Treatment Effect EstimationA Large Scale Heterogeneous Treatment Effect Estimation Framework and Its Applications of Users' Journey at Snap
Heterogeneous Treatment Effect (HTE) and Conditional Average Treatment Effect (CATE) models relax the assumption that treatment effects are the same for every user. We present a large scale industrial framework for estim…
Heterogeneous Treatment Effect EstimationLate Fusion Multi-task Learning for Semiparametric Inference with Nuisance Parameters
In the age of large and heterogeneous datasets, the integration of information from diverse sources is essential to improve parameter estimation. Multi-task learning offers a powerful approach by enabling simultaneous le…
Heterogeneous Treatment Effect EstimationMulti-Task LearningCausalPFN: Amortized Causal Effect Estimation via In-Context Learning
Causal effect estimation from observational data is fundamental across various applications. However, selecting an appropriate estimator from dozens of specialized methods demands substantial manual effort and domain exp…
Decision MakingHeterogeneous Treatment Effect EstimationIn-Context LearningTabPFN: One Model to Rule Them All?
Hollmann et al. (Nature 637 (2025) 319-326) recently introduced TabPFN, a transformer-based deep learning model for regression and classification on tabular data, which they claim "outperforms all previous methods on dat…
AllBayesian InferenceDensity EstimationHeterogeneous Treatment Effect Estimation+2Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising
In higher education, many institutions use algorithmic alerts to flag at-risk students and deliver advising at scale. While much research has focused on evaluating algorithmic predictions, relatively little is known abou…
Heterogeneous Treatment Effect EstimationDynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects
Heterogeneous treatment effect estimation in high-stakes applications demands models that simultaneously optimize precision, interpretability, and calibration. Many existing tree-based causal inference techniques, howeve…
Causal InferenceHeterogeneous Treatment Effect EstimationClass flipping for uplift modeling and Heterogeneous Treatment Effect estimation on imbalanced RCT data
Uplift modeling and Heterogeneous Treatment Effect (HTE) estimation aim at predicting the causal effect of an action, such as a medical treatment or a marketing campaign on a specific individual. In this paper, we focus …
Heterogeneous Treatment Effect EstimationMarketingIs merging worth it? Securely evaluating the information gain for causal dataset acquisition
Merging datasets across institutions is a lengthy and costly procedure, especially when it involves private information. Data hosts may therefore want to prospectively gauge which datasets are most beneficial to merge wi…
Heterogeneous Treatment Effect EstimationPrivacy PreservingStable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations
Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focus on addressing selection bias induced b…
counterfactualHeterogeneous Treatment Effect EstimationRepresentation LearningSelection biasDeep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation
Causal inference has gained much popularity in recent years, with interests ranging from academic, to industrial, to educational, and all in between. Concurrently, the study and usage of neural networks has also grown pr…
Causal InferenceHeterogeneous Treatment Effect EstimationDifferentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
There is a growing interest in estimating heterogeneous treatment effects across individuals using their high-dimensional feature attributes. Achieving high performance in such high-dimensional heterogeneous treatment ef…
Heterogeneous Treatment Effect EstimationRepresentation LearningSelection biasHeterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder
Deep learning models have demonstrated promising results in estimating treatment effects (TEE). However, most of them overlook the variations in treatment outcomes among subgroups with distinct characteristics. This limi…
Heterogeneous Treatment Effect EstimationIndividualized Multi-Treatment Response Curves Estimation using RBF-net with Shared Neurons
Heterogeneous treatment effect estimation is an important problem in precision medicine. Specific interests lie in identifying the differential effect of different treatments based on some external covariates. We propose…
Heterogeneous Treatment Effect EstimationHeterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning
Causal machine learning methods can be used to search for treatment effect heterogeneity in high-dimensional datasets even where we lack a strong enough theoretical framework to select variables or make parametric assump…
Heterogeneous Treatment Effect EstimationUncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model
Estimating heterogeneous treatment effects across individuals has attracted growing attention as a statistical tool for performing critical decision-making. We propose a Bayesian inference framework that quantifies the u…
Bayesian InferenceDecision MakingHeterogeneous Treatment Effect EstimationUncertainty QuantificationFairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making
Causal machine learning methods which flexibly generate heterogeneous treatment effect estimates could be very useful tools for governments trying to make and implement policy. However, as the critical artificial intelli…
Decision MakingFairnessHeterogeneous Treatment Effect Estimation