Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 0 |
| Since 2007 (last 20 years) | 1 |
Descriptor
| Bayesian Statistics | 1 |
| Causal Models | 1 |
| Error of Measurement | 1 |
| Markov Processes | 1 |
| Mathematical Models | 1 |
| Medicine | 1 |
| Monte Carlo Methods | 1 |
| Nonparametric Statistics | 1 |
| Research Problems | 1 |
| Statistical Bias | 1 |
| Statistical Inference | 1 |
| More ▼ | |
Source
| Grantee Submission | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full text
Direct linkDorie, Vincent; Harada, Masataka; Carnegie, Nicole Bohme; Hill, Jennifer – Grantee Submission, 2016
When estimating causal effects, unmeasured confounding and model misspecification are both potential sources of bias. We propose a method to simultaneously address both issues in the form of a semi-parametric sensitivity analysis. In particular, our approach incorporates Bayesian Additive Regression Trees into a two-parameter sensitivity analysis…
Descriptors: Bayesian Statistics, Mathematical Models, Causal Models, Statistical Bias
