Results 11 to 20 of about 18,931,441 (264)

Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments

CoRR
This study examines the educational effect of the Academic Support Center at Kogakuin University. Following the initial assessment, it was suggested that group bias had led to an underestimation of the Center's true impact.
Tomoko Nagai, Takayuki Okuda, Tomoya Nakamura, Yuichiro Sato, Yusuke Sato, Kensaku Kinjo, Kengo Kawamura, Shin Kikuta, Naoto Kumano-go   +8 more
semanticscholar   +4 more sources

Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation

CoRR
The conditional average treatment effect (CATE) is widely used in personalized medicine to inform therapeutic decisions. However, state-of-the-art methods for CATE estimation (so-called meta-learners) often perform poorly in the presence of low overlap ...
Valentyn Melnychuk, Dennis Frauen, J. Schweisthal, S. Feuerriegel   +3 more
semanticscholar   +4 more sources

Conditional Average Treatment Effect Estimation Under Hidden Confounders

CoRR
One of the major challenges in estimating conditional potential outcomes and conditional average treatment effects (CATE) is the presence of hidden confounders.
Ahmed Aloui, Jun-Cheng Dong, Ali Hasan, Vahid Tarokh   +3 more
semanticscholar   +4 more sources

Flexible Machine Learning Estimation of Conditional Average Treatment Effects: A Blessing and a Curse [PDF]

Epidemiology, 2023
Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning methods can be used to study complex forms of causal effect heterogeneity. Recently, several machine learning methods were developed to estimate the conditional average treatment effect (ATE).
Post, Richard A.J., Petkovic, Marko, van den Heuvel, Isabel L., van den Heuvel, Edwin R.   +3 more
openaire   +5 more sources

FoCAT: Foundation Model for Estimating the Conditional Average Treatment Effect

Doklady Mathematics
The paper presents a novel foundation model, FoCAT (Foundation Causal Adaptive Transformer), developed for estimating the conditional treatment effect.
S. Kirpichenko, A. Konstantinov, L. Utkin   +2 more
semanticscholar   +2 more sources

Comparison of random forest methods for conditional average treatment effect estimation with a continuous treatment

Statistical Methods in Medical Research
We are addressing the problem of estimating conditional average treatment effects with a continuous treatment and a continuous response, using random forests.
Sami Tabib, Denis Larocque
semanticscholar   +3 more sources

Proximal Causal Learning of Conditional Average Treatment Effects [PDF]

, 2023
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 currently available.
Erik Sverdrup, Yifan Cui 0001
openaire   +4 more sources

Doubly Robust Uniform Confidence Bands for Group-Time Conditional Average Treatment Effects in Difference-in-Differences [PDF]

Journal of Business & Economic Statistics, 2023
We consider a panel data analysis to examine the heterogeneity in treatment effects with respect to groups, periods, and a pre-treatment covariate of interest in the staggered difference-in-differences setting of Callaway and Sant’Anna.
Shunsuke Imai, L. Qin, Takahide Yanagi
semanticscholar   +3 more sources

Adaptive-TMLE for the average treatment effect based on randomized controlled trial augmented with real-world data [PDF]

Journal of Causal Inference
We consider the problem of estimating the average treatment effect (ATE) when both randomized control trial (RCT) data and external real-world data (RWD) are available.
van der Laan Mark, Qiu Sky, Tarp Jens Magelund, van der Laan Lars   +3 more
doaj   +2 more sources

DeepBlip: Estimating Conditional Average Treatment Effects Over Time

CoRR
Structural nested mean models (SNMMs) are a principled approach to estimate the treatment effects over time. A particular strength of SNMMs is to break the joint effect of treatment sequences over time into localized, time-specific ``blip effects''. This
Hao-Rui Ma, Dennis Frauen, S. Feuerriegel   +2 more
semanticscholar   +3 more sources