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Conditional average treatment effect estimation with marginally constrained models [PDF]
Journal of Causal Inference, 2023 Treatment effect estimates are often available from randomized controlled trials as a single average treatment effect for a certain patient population.
van Amsterdam Wouter A. C. +1 more
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Implications of acute change in estimated Glomerular Filtration Rate (eGFR) for the effect of sodium-glucose cotransporter-2 inhibitors (SGLT-2i) on long-term endpoints. [PDF]
PLoS ONEIn randomized trials, the primary analysis often estimates the average treatment effect on a clinical endpoint. Some treatments also lead to early changes in a biomarker that is prognostic for the clinical endpoint, prompting investigators to explore how
Ransmond O Berchie +4 more
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Comparison of random forest methods for conditional average treatment effect estimation with a continuous treatment. [PDF]
Stat Methods Med ResWe are addressing the problem of estimating conditional average treatment effects with a continuous treatment and a continuous response, using random forests. We explore two general approaches: building trees with a split rule that seeks to increase the heterogeneity of the treatment effect estimation and building trees to predict [Formula: see text ...
Tabib S, Larocque D.
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Estimating Conditional Average Treatment Effects with Missing Treatment Information
CoRR, 2022 Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention. In this paper, we analyze CATE estimation in the setting with missing treatments where unique challenges arise
Kuzmanovic, Milan +2 more
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Estimations of the Conditional Tail Average Treatment Effect [PDF]
SSRN Electronic Journal, 2020 We study estimation of the conditional tail average treatment effect (CTATE), defined as a difference between conditional tail expectations of potential outcomes. The CTATE can capture heterogeneity and deliver aggregated local information of treatment effects over different quantile levels, and is closely related to the notion of second order ...
Le‐Yu Chen, Yu-Min Yen
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On IPW-based estimation of conditional average treatment effects
Journal of Statistical Planning and Inference, 2021 The research in this paper gives a systematic investigation on the asymptotic behaviours of four inverse probability weighting (IPW)-based estimators for conditional average treatment effect, with nonparametrically, semiparametrically, parametrically estimated and true propensity score, respectively.
Zhou, Niwen, Zhu, Lixing
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Estimation of Conditional Average Treatment Effects With High-Dimensional Data [PDF]
Journal of Business & Economic Statistics, 2020 Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the ...
FAN, Qingliang +3 more
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Personalized decision making – A conceptual introduction
Journal of Causal Inference, 2023 Personalized decision making targets the behavior of a specific individual, while population-based decision making concerns a subpopulation resembling that individual.
Mueller Scott, Pearl Judea
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Estimating Conditional Average Treatment Effects [PDF]
Journal of Business & Economic Statistics, 2014 We consider a functional parameter called the conditional average treatment effect (CATE), designed to capture the heterogeneity of a treatment effect across subpopulations when the unconfoundedness assumption applies. In contrast to quantile regressions, the subpopulations of interest are defined in terms of the possible values of a set of continuous ...
Jason Abrevaya +2 more
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Optimal weighting for estimating generalized average treatment effects
Journal of Causal Inference, 2022 In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects.
Kallus Nathan, Santacatterina Michele
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