Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model

1 code implementation • 16 Dec 2023 • Shunsuke Horii, Yoichi Chikahara

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.

Bayesian Inference Decision Making +1

Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributions

no code implementations • 15 Mar 2021 • Shunsuke Horii

In the estimation of the causal effect under linear Structural Causal Models (SCMs), it is common practice to first identify the causal structure, estimate the probability distributions, and then calculate the causal effect.