A Comparison of Matching and Weighting Methods for Causal Inference Based on Routine Health Insurance Data, Or: What to Do if an RCT Is Impossible

Gesundheitswesen - Germany
doi 10.1055/a-1009-6634
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Georg Thieme Verlag KG

Related search

The Balance-Sample Size Frontier in Matching Methods for Causal Inference

American Journal of Political Science
SociologyInternational RelationsPolitical Science
2016English

Causal Inference in Cancer Epidemiology: What Is the Role of Mendelian Randomization?

Cancer Epidemiology Biomarkers and Prevention
MedicineEpidemiologyOncology
2018English

What Good Is Legislation--Or Planning--If We Can't Make It Work? The Need for a Comprehensive Approach to Health and Welfare.

American Journal of Public Health
EnvironmentalPublic HealthOccupational Health
1982English

What, if Anything, Is an Evolutionary Novelty?

Philosophy of Science
PhilosophyPhilosophy of ScienceHistory
2008English

Erratum To: Artificial Neural Networks and Their Potentialities in Analyzing Budget Health Data: An Application for Italy of What-If Theory

Quality and Quantity
StatisticsSocial SciencesProbability
2016English

Health Center–Based Community-Supported Agriculture: An RCT

American Journal of Preventive Medicine
EpidemiologyPublic HealthOccupational HealthEnvironmental
2019English

Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference

2019English

What if What You Are Looking for Is Not There?

Journal of Applied Physiology
MedicinePhysiologySports Science
2019English

Improving the Analysis of Routine Outcome Measurement Data: What a Bayesian Approach Can Do for You

International Journal of Methods in Psychiatric Research
PsychiatryMental Health
2015English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy