PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect
Given a dataset of individuals each described by a covariate vector, a treatment, and an
observed outcome on the treatment, the goal of the individual treatment effect (ITE) …
observed outcome on the treatment, the goal of the individual treatment effect (ITE) …
Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation
Estimating conditional average dose responses (CADR) is an important but challenging
problem. Estimators must correctly model the potentially complex relationships between …
problem. Estimators must correctly model the potentially complex relationships between …
Disentangled Representation via Variational AutoEncoder for Continuous Treatment Effect Estimation
R Cui, J Sun, B He, K Yang, B Ge - arxiv preprint arxiv:2406.02310, 2024 - arxiv.org
Continuous treatment effect estimation holds significant practical importance across various
decision-making and assessment domains, such as healthcare and the military. However …
decision-making and assessment domains, such as healthcare and the military. However …
Subspace Learning for Conditional Average Treatment Effect Estimation with Unmeasured Confounding
H Qiao - AAAI 2025 Workshop on Artificial Intelligence with …, 2025 - openreview.net
Conditional average treatment effect (CATE) is the average causal effect of a treatment or an
intervention (eg, medication) on the outcome of interest, conditional on subjects' covariates …
intervention (eg, medication) on the outcome of interest, conditional on subjects' covariates …
DTRNet: Precisely Correcting Selection Bias in Individual-Level Continuous Treatment Effect Estimation by Reweighted Disentangled Representation
Estimating the individual-level continuous treatment effect holds significant practical
importance in various decision-making domains, such as personalized healthcare and …
importance in various decision-making domains, such as personalized healthcare and …