Hyperparameter tuning and model evaluation in causal effect estimation
The performance of most causal effect estimators relies on accurate predictions of high-
dimensional non-linear functions of the observed data. The remarkable flexibility of modern …
dimensional non-linear functions of the observed data. The remarkable flexibility of modern …
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding
MG Marmarelis, G Ver Steeg… - Causal Learning …, 2024 - proceedings.mlr.press
Causal inference of exact individual treatment outcomes in the presence of hidden
confounders is rarely possible. Recent work has extended prediction intervals with finite …
confounders is rarely possible. Recent work has extended prediction intervals with finite …
Tighter Prediction Intervals for Causal Outcomes Under Hidden Confounding
Causal inference of exact individual treatment outcomes in the presence of hidden
confounders is rarely possible. Instead, recent work has adapted conformal prediction to …
confounders is rarely possible. Instead, recent work has adapted conformal prediction to …
Undersmoothing Causal Estimators with Generative Trees
Average causal effects are averages of (heterogeneous) individual treatment effects (ITEs)
taken over the entire target population. The estimation of average causal effects has been …
taken over the entire target population. The estimation of average causal effects has been …
Understanding hyperparameters in machine learning for causal estimation from observational data
D Machlanski - 2024 - repository.essex.ac.uk
Causal analysis is fundamental to science and decision-making. It unravels the structure of
the process underlying the data and estimates the effectiveness of interventions. Deriving …
the process underlying the data and estimates the effectiveness of interventions. Deriving …
[引用][C] The Importance of Hyperparameter Tuning in Causal Effect Estimation
D Machlanski, S Samothrakis, P Clarke