Heterogeneous Treatment Effect Estimation
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Benchmarks
IHDP
Most implemented
Generalized Random Forests
Estimating individual treatment effect: generalization bounds and algorithms
Local Linear Forests
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data
On Inductive Biases for Heterogeneous Treatment Effect Estimation
Quasi-Oracle Estimation of Heterogeneous Treatment Effects
Papers
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 Learning