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Found 5 papers, 1 papers with code
Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
There is a growing interest in estimating heterogeneous treatment effects across individuals using their high-dimensional feature attributes.
Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model
We propose a Bayesian inference framework that quantifies the uncertainty in treatment effect estimation to support decision-making in a relatively small sample size setting.
Meta-learning for heterogeneous treatment effect estimation with closed-form solvers
With our formulation, we can obtain optimal task-specific parameters in a closed form that are differentiable with respect to task-shared parameters, making it possible to perform effective meta-learning.
Feature Selection for Discovering Distributional Treatment Effect Modifiers
Finding the features relevant to the difference in treatment effects is essential to unveil the underlying causal mechanisms.
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint
To avoid restrictive functional assumptions, we define the {\it probability of individual unfairness} (PIU) and solve an optimization problem where PIU's upper bound, which can be estimated from data, is controlled to be close to zero.
