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
doaj +9 more sources
CATE Meets ML - Conditional Average Treatment Effect and Machine Learning [PDF]
SSRN Electronic Journal, 2021 Abstract For treatment effects—one of the core issues in modern econometric analysis—prediction and estimation are two sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models.
D. Jacob
semanticscholar +7 more sources
Estimation of the Local Conditional Tail Average Treatment Effect [PDF]
Journal of Business & Economic Statistics, 2021 The conditional tail average treatment effect (CTATE) is defined as a difference between the conditional tail expectations of potential outcomes, which can capture heterogeneity and deliver aggregated local information on treatment effects over different
Le-Yu Chen, Yu-Min Yen
semanticscholar +4 more sources
Generalization bounds and algorithms for estimating conditional average treatment effect of dosage [PDF]
CoRR, 2022 We investigate the task of estimating the conditional average causal effect of treatment-dosage pairs from a combination of observational data and assumptions on the causal relationships in the underlying system.
Alexis Bellot, Anish Dhir, Giulia Prando
semanticscholar +4 more sources
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression [PDF]
CoRR, 2022 We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE), with linear regression models. As the development of machine learning for causal inference, a wide range of large-scale models for causality are ...
Masahiro Kato, M. Imaizumi
semanticscholar +4 more sources
On IPW-based estimation of conditional average treatment effect [PDF]
Journal of Statistical Planning and Inference, 2020 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 ...
Niwen Zhou, Lixing Zhu
semanticscholar +4 more sources
tehtuner: An R package to fit and tune models for the conditional average treatment effect
Journal of Open Source Software, 2023 Randomized clinical trials (RCTs) often test and describe the average treatment effect, or how the candidate intervention is expected to increase or decrease an outcome of interest for all patients in the population of interest.
Jack M. Wolf
semanticscholar +2 more sources
BOUNDS ON THE CONDITIONAL AND AVERAGE TREATMENT EFFECT WITH UNOBSERVED CONFOUNDING FACTORS [PDF]
The Annals of Statistics, 2018 For observational studies, we study the sensitivity of causal inference when treatment assignments may depend on unobserved confounders. We develop a loss minimization approach for estimating bounds on the conditional average treatment effect (CATE) when
Steve Yadlowsky +4 more
semanticscholar +4 more sources
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators [PDF]
Advances in Neural Information Processing Systems 37The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators have been developed with advancements in machine learning and causal ...
Yi-Yan Huang +4 more
semanticscholar +4 more sources
Outcome regression-based estimation of conditional average treatment effect [PDF]
Annals of the Institute of Statistical Mathematics, 2020 The research is about a systematic investigation on the following issues. First, we construct different outcome regression-based estimators for conditional average treatment effect under, respectively, true, parametric, nonparametric and semiparametric ...
Luquan Li, Niwen Zhou, Lixing Zhu
semanticscholar +4 more sources