LR
L.R. Ritter
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Robust Causal Inference with Multi-task Gaussian Processes
Enhancing Generalization and Calibration through Data-Aware Kernel and Prior Design
Causal Multi-task Gaussian Processes (CMGPs) provide a Bayesian approach for estimating in-
dividualized treatment effects by modeling potential outcomes as correlated functions. However,
they struggle under high-dimensionality and treatment imbalance, leading to overfitting and unre-
liable uncertainty estimates. This study examines two failure modes: poor generalization in high-
dimensional spaces and overconfident predictions in low-overlap regions. To address these, two
data-aware enhancements are proposed: an overlap-adaptive kernel that scales similarity based on
local treatment density, and a regularized prior that down-weights unstable features using marginal
treatment effect variance. Evaluations on synthetic data and the IHDP benchmark show improved
effect estimation, credible interval calibration, and robustness in challenging settings. These find-
ings highlight practical strategies for enhancing CMGPs in real-world causal inference tasks. ...
dividualized treatment effects by modeling potential outcomes as correlated functions. However,
they struggle under high-dimensionality and treatment imbalance, leading to overfitting and unre-
liable uncertainty estimates. This study examines two failure modes: poor generalization in high-
dimensional spaces and overconfident predictions in low-overlap regions. To address these, two
data-aware enhancements are proposed: an overlap-adaptive kernel that scales similarity based on
local treatment density, and a regularized prior that down-weights unstable features using marginal
treatment effect variance. Evaluations on synthetic data and the IHDP benchmark show improved
effect estimation, credible interval calibration, and robustness in challenging settings. These find-
ings highlight practical strategies for enhancing CMGPs in real-world causal inference tasks. ...
Causal Multi-task Gaussian Processes (CMGPs) provide a Bayesian approach for estimating in-
dividualized treatment effects by modeling potential outcomes as correlated functions. However,
they struggle under high-dimensionality and treatment imbalance, leading to overfitting and unre-
liable uncertainty estimates. This study examines two failure modes: poor generalization in high-
dimensional spaces and overconfident predictions in low-overlap regions. To address these, two
data-aware enhancements are proposed: an overlap-adaptive kernel that scales similarity based on
local treatment density, and a regularized prior that down-weights unstable features using marginal
treatment effect variance. Evaluations on synthetic data and the IHDP benchmark show improved
effect estimation, credible interval calibration, and robustness in challenging settings. These find-
ings highlight practical strategies for enhancing CMGPs in real-world causal inference tasks.
dividualized treatment effects by modeling potential outcomes as correlated functions. However,
they struggle under high-dimensionality and treatment imbalance, leading to overfitting and unre-
liable uncertainty estimates. This study examines two failure modes: poor generalization in high-
dimensional spaces and overconfident predictions in low-overlap regions. To address these, two
data-aware enhancements are proposed: an overlap-adaptive kernel that scales similarity based on
local treatment density, and a regularized prior that down-weights unstable features using marginal
treatment effect variance. Evaluations on synthetic data and the IHDP benchmark show improved
effect estimation, credible interval calibration, and robustness in challenging settings. These find-
ings highlight practical strategies for enhancing CMGPs in real-world causal inference tasks.