<--- Back to Details
First PageDocument Content
Design of experiments / Causal inference / Evaluation / Observational study / Methodology / Knowledge / Impact assessment / Confounding / Experiment / Quasi-experiment / Program evaluation / Impact evaluation
Date: 2018-05-30 16:17:25
Design of experiments
Causal inference
Evaluation
Observational study
Methodology
Knowledge
Impact assessment
Confounding
Experiment
Quasi-experiment
Program evaluation
Impact evaluation

A2J Evaluation and Research Options Process Evaluation Formative Assessment

Add to Reading List

Source URL: a2jlab.org

Download Document from Source Website

File Size: 215,62 KB

Share Document on Facebook

Similar Documents

Adjusting for Confounding with Text Matching∗ Margaret E. Roberts†, Brandon M. Stewart‡, and Richard A. Nielsen§ February 27, 2018¶ Abstract We identify situations in which conditioning on text can address confou

Adjusting for Confounding with Text Matching∗ Margaret E. Roberts†, Brandon M. Stewart‡, and Richard A. Nielsen§ February 27, 2018¶ Abstract We identify situations in which conditioning on text can address confou

DocID: 1xTCd - View Document

Multicausality: Confounding - Assignment solutions 1. a. Reserpine is a risk factor. Overall, the incidence of breast cancer isper 100,000 women-years in reserpine users and 6.14 per 100,000 women-years in nonuser

Multicausality: Confounding - Assignment solutions 1. a. Reserpine is a risk factor. Overall, the incidence of breast cancer isper 100,000 women-years in reserpine users and 6.14 per 100,000 women-years in nonuser

DocID: 1vpNA - View Document

Algorithmic Decision Making in the Presence of Unmeasured Confounding Jongbin Jung Stanford University

Algorithmic Decision Making in the Presence of Unmeasured Confounding Jongbin Jung Stanford University

DocID: 1v3Zm - View Document

13. Multicausality – analysis approaches Concepts and methods for analyzing epidemiologic data involving more than two variables; control of confounding through stratified analysis and mathematical modeling. Multivaria

13. Multicausality – analysis approaches Concepts and methods for analyzing epidemiologic data involving more than two variables; control of confounding through stratified analysis and mathematical modeling. Multivaria

DocID: 1uNxO - View Document

Introduction to matching Tuesday, November 13, 2012 9:09 PM • In brief, matching is a way of non‐parametrically controlling for confounding  variables

Introduction to matching Tuesday, November 13, 2012 9:09 PM • In brief, matching is a way of non‐parametrically controlling for confounding  variables

DocID: 1uuCK - View Document