Papers Heterogeneous Treatment Effect Estimation
“Heterogeneous Treatment Effect Estimation” 태그가 달린 논문 44편 · 필터 해제
Meta-learning for heterogeneous treatment effect estimation with closed-form solvers
This article proposes a meta-learning method for estimating the conditional average treatment effect (CATE) from a few observational data. The proposed method learns how to estimate CATEs from multiple tasks and uses the…
FormHeterogeneous Treatment Effect EstimationMeta-LearningUnderstanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data
We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events. Albeit its great practical relevance, this problem has received little attention compa…
Heterogeneous Treatment Effect EstimationIn Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation
Personalized treatment effect estimates are often of interest in high-stakes applications -- thus, before deploying a model estimating such effects in practice, one needs to be sure that the best candidate from the ever-…
counterfactualHeterogeneous Treatment Effect EstimationModel SelectionData-Driven Estimation of Heterogeneous Treatment Effects
Estimating how a treatment affects different individuals, known as heterogeneous treatment effect estimation, is an important problem in empirical sciences. In the last few years, there has been a considerable interest i…
counterfactualHeterogeneous Treatment Effect EstimationHeterogeneous Treatment Effect Estimation for Observational Data using Model-based Forests
The estimation of heterogeneous treatment effects (HTEs) has attracted considerable interest in many disciplines, most prominently in medicine and economics. Contemporary research has so far primarily focused on continuo…
Heterogeneous Treatment Effect EstimationImproving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty
Estimating how a treatment affects units individually, known as heterogeneous treatment effect (HTE) estimation, is an essential part of decision-making and policy implementation. The accumulation of large amounts of dat…
Decision Makingfeature selectionHeterogeneous Treatment Effect EstimationBenchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
Estimating personalized effects of treatments is a complex, yet pervasive problem. To tackle it, recent developments in the machine learning (ML) literature on heterogeneous treatment effect estimation gave rise to many …
BenchmarkingFeature ImportanceHeterogeneous Treatment Effect EstimationEfficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes
Learning heterogeneous treatment effects (HTEs) is an important problem across many fields. Most existing methods consider the setting with a single treatment arm and a single outcome metric. However, in many real world …
Heterogeneous Treatment Effect EstimationFlexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles
Contextual bandit algorithms often estimate reward models to inform decision-making. However, true rewards can contain action-independent redundancies that are not relevant for decision-making. We show it is more data-ef…
Decision MakingHeterogeneous Treatment Effect EstimationMulti-Armed BanditsGCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace
Uplift modeling is a rapidly growing approach that utilizes causal inference and machine learning methods to directly estimate the heterogeneous treatment effects, which has been widely applied to various online marketpl…
Causal InferenceDecision MakingHeterogeneous Treatment Effect EstimationExploring Transformer Backbones for Heterogeneous Treatment Effect Estimation
Previous works on Treatment Effect Estimation (TEE) are not in widespread use because they are predominantly theoretical, where strong parametric assumptions are made but untractable for practical application. Recent wor…
Heterogeneous Treatment Effect EstimationPOSSelection biasHeterogeneous 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. Online games regularly update their software to fix bugs, add functionality that improve the g…
Causal InferenceHeterogeneous Treatment Effect EstimationOpen-Ended Question AnsweringGCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods
Heterogeneous treatment effect (HTE) estimation with continuous treatment is essential in multiple disciplines, such as the online marketplace and pharmaceutical industry. The existing machine learning (ML) methods, lik…
Heterogeneous Treatment Effect EstimationHeterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark
Developing new drugs for target diseases is a time-consuming and expensive task, drug repurposing has become a popular topic in the drug development field. As much health claim data become available, many studies have be…
BIG-bench Machine LearningCausal InferenceEconometricsHeterogeneous Treatment Effect EstimationThe Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest
A common belief about the growing medium of livestreaming is that its value lies in its "live" component. We examine this belief by comparing how the price elasticity of demand for live events varies before, on the day o…
Heterogeneous Treatment Effect EstimationOn Inductive Biases for Heterogeneous Treatment Effect Estimation
We investigate how to exploit structural similarities of an individual's potential outcomes (POs) under different treatments to obtain better estimates of conditional average treatment effects in finite samples. Especial…
AllHeterogeneous Treatment Effect EstimationInductive BiasPOSCounterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation
Learning-to-Rank (LTR) models trained from implicit feedback (e.g. clicks) suffer from inherent biases. A well-known one is the position bias -- documents in top positions are more likely to receive clicks due in part to…
counterfactualHeterogeneous Treatment Effect EstimationLearning-To-RankPositionLearning Triggers for Heterogeneous Treatment Effects
The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous …
Heterogeneous Treatment Effect EstimationRecommendation SystemsNon-Parametric Inference Adaptive to Intrinsic Dimension
We consider non-parametric estimation and inference of conditional moment models in high dimensions. We show that even when the dimension $D$ of the conditioning variable is larger than the sample size $n$, estimation an…
Heterogeneous Treatment Effect EstimationLocal Linear Forests
Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects. Taking the p…
Causal InferenceHeterogeneous Treatment Effect Estimationregressionvalid