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pro vyhledávání: '"Sverdrup, Erik"'
Flexible machine learning tools are being used increasingly to estimate heterogeneous treatment effects. This paper gives an accessible tutorial demonstrating the use of the causal forest algorithm, available in the R package grf. We start with a bri
Externí odkaz:
http://arxiv.org/abs/2409.01578
Autor:
Sverdrup, Erik, Wager, Stefan
This article walks through how to estimate conditional average treatment effects (CATEs) with right-censored time-to-event outcomes using the function causal_survival_forest (Cui et al., 2023) in the R package grf (Athey et al., 2019, Tibshirani et a
Externí odkaz:
http://arxiv.org/abs/2312.02482
Qini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of the Qini curve to multiple costly treatment arms, that quantifies the va
Externí odkaz:
http://arxiv.org/abs/2306.11979
Autor:
Sverdrup, Erik, Cui, Yifan
Efficiently and flexibly estimating treatment effect heterogeneity is an important task in a wide variety of settings ranging from medicine to marketing, and there are a considerable number of promising conditional average treatment effect estimators
Externí odkaz:
http://arxiv.org/abs/2301.10913
Publikováno v:
Working Papers (Faculty) - Stanford Graduate School of Business. Oct2024, p1-29. 29p.
Estimation of conditional average treatment effects (CATEs) plays an essential role in modern medicine by informing treatment decision-making at a patient level. Several metalearners have been proposed recently to estimate CATEs in an effective and f
Externí odkaz:
http://arxiv.org/abs/2207.07758
Autor:
Dandl, Susanne, Hothorn, Torsten, Seibold, Heidi, Sverdrup, Erik, Wager, Stefan, Zeileis, Achim
Estimation of heterogeneous treatment effects (HTE) is of prime importance in many disciplines, ranging from personalized medicine to economics among many others. Random forests have been shown to be a flexible and powerful approach to HTE estimation
Externí odkaz:
http://arxiv.org/abs/2206.10323
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in a survival a
Externí odkaz:
http://arxiv.org/abs/2001.09887
Missing attributes are ubiquitous in causal inference, as they are in most applied statistical work. In this paper, we consider various sets of assumptions under which causal inference is possible despite missing attributes and discuss corresponding
Externí odkaz:
http://arxiv.org/abs/1910.10624
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